- Research Article
- Open Access
A Sparsity-Based Approach to 3D Binaural Sound Synthesis Using Time-Frequency Array Processing
© Maximo Cobos et al. 2010
- Received: 2 March 2010
- Accepted: 7 September 2010
- Published: 14 September 2010
Localization of sounds in physical space plays a very important role in multiple audio-related disciplines, such as music, telecommunications, and audiovisual productions. Binaural recording is the most commonly used method to provide an immersive sound experience by means of headphone reproduction. However, it requires a very specific recording setup using high-fidelity microphones mounted in a dummy head. In this paper, we present a novel processing framework for binaural sound recording and reproduction that avoids the use of dummy heads, which is specially suitable for immersive teleconferencing applications. The method is based on a time-frequency analysis of the spatial properties of the sound picked up by a simple tetrahedral microphone array, assuming source sparseness. The experiments carried out using simulations and a real-time prototype confirm the validity of the proposed approach.
- Sound Source
- Microphone Array
- Synthesis Stage
- Microphone Signal
- Music Signal
Human hearing plays a major role in the way our environment is perceived. Generally, sound is perceived in all three dimensions, width, height, and depth, which are all necessary to achieve a natural perception of sound . These attributes are usually employed to describe the spatial characteristics of sound taking into account its diffuseness properties. The human auditory system is very sophisticated and, thus, capable to analyze and extract most spatial information pertaining to a sound source using two ears. In fact, when a sound scene is recorded by a single microphone, we are still able to recognize the original sound events. However, much of the information corresponding to the spatial properties of these events is lost. As a result, spatial sound recording and reproduction techniques are always based on a multichannel approach.
Reproduction using two-channels or stereo is the most common way that most people know to convey some spatial content into sound recording and reproduction, and this can be considered as the simplest approximation to spatial sound. On the other hand, surround sound systems have evolved and entered homes in order to give a better sensation than stereo by using more reproduction channels and have been widely utilized in theaters since the middle 70s. Both stereo and surround systems have an optimal listening position, known as sweet spot . This optimum listening area is almost limited to the central point in the loudspeaker setup. Outside the central zone, the perceived virtual source locations differ significantly from their intended spatial position.
Another much more realistic strategy is to reproduce directly in the ears of the listener, via headphones, the signal that he/she would perceive in the acoustic environment that is intended to be simulated. This strategy is widely known as binaural reproduction. The signals to be reproduced with headphones can be recorded with an acoustic dummy head or they can be artificially synthesized by using a measured Head-Related Transfer-Function (HRTF) . In an anechoic environment, as sound propagates from the source to the listener, its own head, pinna, and torso introduce changes to the sound before it reaches the ear drums. These effects of the listener's body are registered by the HRTF, which is the transfer function between the sound pressure that is present at the center of the listener's head when the listener is absent and the sound pressure developed at the listener's ear. Since humans have different-sized heads, torsos, and ear shapes, HRTFs vary from person to person. The HRTF is a function of direction, distance, and frequency. The inverse Fourier transform of the HRTF is the Head-Related Impulse Response (HRIR), which is a function of direction, distance, and time. Using binaural sound reproduction, it is possible to create a very convincing and immersive sound experience that provides the listener with a natural perception of localized sound events.
In this paper, we present a novel method to capture and process the spatial characteristics of sound with the aim of providing a real-time 3D audio experience. Instead of using an expensive dummy head setup, a small tetrahedral microphone array is utilized to discriminate among the three spatial dimensions, providing a cheap and effective way of constructing a full 3D audio system. The proposed technique is based on a two-step approach. In a first analysis stage, the signals captured by each microphone pair are processed in the time-frequency domain, resulting in a complete directional description of the recorded sound. In the synthesis stage, source sparseness is assumed, and each time-frequency bin is selectively filtered using a different HRTF depending on its estimated direction.
The signals obtained by the microphones of the array enter the analysis stage.
In the analysis stage, the four signals are first transformed into the time-frequency domain by means of the Short-Time Fourier Transform (STFT). Then, Direction-of-Arrival (DOA) information (azimuth and elevation) for each time-frequency bin is extracted using the processing described in Section 3.
The synthesis stage is based on a time-frequency selective HRTF filtering of one of the input microphone signals. This filtering is carried out selectively in the STFT domain according to the directions estimated in the analysis stage, resulting in the output signals for the left and right ears. Finally, the ear signals are transformed back to the time domain using the inverse STFT operator.
The paper is structured as follows. Section 2 provides a review of multisource binaural synthesis techniques closely related to our work. Section 3 presents the processing techniques involved in the analysis stage of the method, describing the signal model and the array geometry used to estimate the directional information. Section 4 is devoted to the synthesis stage of the method, where the analyzed spatial information is used to create an immersive 3D sound scene. Section 5 presents a performance comparison between conventional binaural reproduction and our sparsity-based approach using synthetic mixtures of speech and music sources. Section 6 describes and evaluates a real-time prototype that implements the processing described in this paper. Finally, in Section 7, the conclusions of this work are summarized.
2.1. Multisource Binaural Sound Synthesis
It is widely known that binaural sound synthesis is a technique capable of reproducing a virtual sound image of a recorded sound signal at an apparent position in the three-dimensional space. Binaural synthesis is based on the use of HRTFs (or their HRIRs time-domain representation) to filter the audio streams corresponding to different sound sources located at different spatial positions, creating a highly immersive audio experience. As a result, to render sound sources positioned at different locations it is necessary to use filters ( for the left ear and for the right ear). The computational complexity is therefore very dependent on the number of sound sources, which makes the real-time rendering of multiple sound sources a very intensive computational task [4, 5]. In this context, many approaches have been proposed to reduce the complexity of multisource binaural synthesis, many of them based on parametric HRTFs . Experiments with parametric HRTFs have confirmed that subjects cannot discriminate the parametric HRTF versions from the original ones if a suitable set of parameters are selected within each critical band . Breebaart et al.  proposed some methods to provide a multichannel audio experience over stereo headphones from a mixdown of sound source signals and a parametric representation (spatial parameters) of the multichannel original signal in a time-frequency domain. The binaural synthesis stage combines the spatial parameters of the multichannel signal with the HRTF parameters that describe the virtual loudspeaker setup, resulting in a set of combined binaural parameters that are later used to modify the downmix signal. These rendering methods provide high-quality binaural reproduction of multichannel audio and can be easily combined with multichannel audio coders such as MPEG surround.
Despite being powerful and promising, the above approaches are substantially different from the application covered in this paper. The reason is that they are mainly based on a time-frequency analysis of different loudspeaker signals whereas our proposed method takes as input the signals from a small microphone array, which are successfully employed to describe the sound field in one point of the three-dimensional space. Therefore, the proposed method shares more similarities with another spatial sound processing technique known as Directional Audio Coding.
2.2. Directional Audio Coding
Directional Audio Coding (DirAC) is a recently proposed method for spatial sound recording and reproduction  which shares many similarities with the binaural synthesis technique described in this paper. In a first analysis stage, DirAC uses typically a B-format microphone to capture the spatial properties of the sound recorded in a given environment (although other alternatives can also be used ). In a second stage, the analyzed spatial features are employed to reproduce the recorded sound again by means of an arbitrary loudspeaker setup. Note that although B-format signals are used, there are substantial differences with conventional Ambisonics reproduction .
More recently, a binaural synthesis version of DirAC has been proposed to provide spatial sound reproduction over headphones using the conventional DirAC scheme . The main features of this version and their relation to our proposed approach are next discussed.
2.2.1. DirAC Analysis and Synthesis
The analysis stage of DirAC is based on a time-frequency processing of the B-format input signals to estimate direction and diffuseness parameters. To this end, an energetic analysis based on pressure and velocity signals is carried out, which needs for an adequate calibration before starting the processing . Besides using a B-format microphone, different array structures can be employed in this analysis stage with the aim of estimating the necessary direction and diffuseness parameters.
Regarding DirAC synthesis, several alternatives have also been proposed. In the low-bit-rate version, only one omnidirectional signal is transmitted along with the spatial metadata, which is used as the signal that is processed and applied to all the reproduction loudspeakers. Another version uses B-format signals to construct a set of virtual microphone signals that are similarly processed using the metadata obtained from the analysis stage .
The transmitted signals are divided into two different streams: the diffuse and the nondiffuse sound stream. The nondiffuse sound is assumed to be the part of sound that has a clear direction and is reproduced by the loudspeaker setup using vector base amplitude panning (VBAP) . In contrast, the diffuse sound stream is assumed to surround the listener and the input signal is decorrelated and played from multiple loudspeakers.
The binaural version of DirAC follows a philosophy similar to that of Breebaart's work in that a virtual loudspeaker setup is assumed and implemented by means of HRTF data. Both diffuse and nondiffuse sound streams are processed in the same way as in the real loudspeaker version but using virtual loudspeakers simulated by means of HRTFs .
2.2.2. Relation to the Proposed Approach
As previously commented, DirAC shares many similarities with the binaural synthesis method proposed in this paper, which is also based on a two-step approach. However, substantial differences can be found both in the analysis and the synthesis stages of the algorithm.
As will be seen in Section 3.3, amplitude calibration is not necessary in our proposed analysis stage, since DOA estimation is based only on phase information. Although different microphone array alternatives have already been proposed for DOA estimation in a DirAC context, they either are limited to DOA estimation in the horizontal plane  or they use more than 4 microphones [17, 18]. Moreover, as will be later explained, diffuseness is not directly estimated in our proposed approach since the synthesis stage does not rely on this parameter.
On the other hand, the synthesis stage does not assume a virtual loudspeaker setup nor makes a different treatment between diffuse and nondiffuse components. This makes the synthesis processing even more simple than in DirAC. In fact, in our method, diffuseness information is assumed to be inherently encoded by the DOA estimates since the variance found on the directional information over the time-frequency domain is already a representation of the diffuseness characteristics of the recorded sound. In this context, there is no need for assuming a specific loudspeaker reproduction setup since each time-frequency element is binaurally reproduced according to its estimated direction.
3.1. Signal Model
where is the signal recorded at the th microphone at time sample , is the th source signal, is the impulse response of the acoustic path from source to sensor , and is the maximum length of all impulse responses.
where denotes the STFT of the th microphone signal, being and the frequency index and time frame index, respectively. denotes the STFT of the source signal and is the frequency response from source to sensor . Note that (2) is only equivalent to (1) in the case when the analysis window in the computation of the STFT is longer than .
3.2. Sparsity and Disjointness
Speech and music signals have been shown to be sparse in the time-frequency domain . A sparse source has a peaky probability density function; the signal is close to zero at most time-frequency points, and has large values in rare occasions. This property has been widely applied in many works related to source signal localization [21, 22] and separation [23, 24] in underdetermined situations, that is, when there are more sources than microphone signals. However, source sparsity alone is useless if the sources overlap to a high degree. The disjointness of a mixture of sources can be defined as the degree of nonoverlapping of the mixed signals. An objective measure of disjointness is the so-called W-Disjoint Orthogonality (WDO) [25, 26].
Spectral overlapping depends not only on source sparsity, but also on the mutual relationships between signals. Highly uncorrelated signals will result in a low probability of overlapping. This is even truer for statistically independent signals, since independence is a stronger requirement than uncorrelation. Speech signals most often mix in a random and uncorrelated manner, such as in the cocktail party paradigm. With music mixtures, the situation is different. Their disjointness will vary strongly according to music type. Tonal music will result in strong overlaps in frequency, while atonal music will be more disjoint in frequency .
The disjointness properties of speech and music signals are dependent on the window size parameter, which affects the number of frequency bands in the analysis. In particular, the disjointness of speech signals decreases when the window size is very large as a consequence of the reduced temporal resolution. For music signals, frequency disjointness plays a more important role than time disjointness and so frequency resolution should be favored with longer analysis windows. Moreover, as expected, mixtures of correlated melodies shown to be less disjoint than uncorrelated ones due to the higher amount of spectral and temporal overlapping.
It is also worth to remark that the sparsity and disjointness properties of audio signals become affected in reverberant environments. The room impulse response smears the energy in both time and frequency and so the spectral overlap between different sources in the time-frequency domain is increased with reverberation. Despite this effect, the assumption of nonoverlapping sources has been shown to be still useful for sparsity-based applications such as source separation [28, 29].
3.3. Array Geometry and DOA Estimation
Note that for each time-frequency point , estimating the 3D direction of arrival is relatively simple, just using the observed phase differences between 3 microphone pairs of the array. Another aspect to consider is spatial aliasing. The distance between microphones determines the angular aliasing frequency. Due to the ambiguity in the calculation of the phase differences, the maximum ambiguity-free frequency in a microphone pair subarray would be given by , where is the separation distance between the capsules. Beyond this frequency, there is not a one-to-one relationship between phase difference and spatial direction. However, small arrays with cm provide an unambiguous bandwidth greater than 11 kHz, covering a perceptually important frequency range.
As said in Section 1, HRTFs provide accurate localization cues because they encode all the necessary information regarding how the arriving sound is filtered by the diffraction and reflection properties of the head, pinna, and torso, before it reaches the eardrum and inner ear. Using this information, synthesizing a binaural sound signal for headphone reproduction is straightforward. The HRTF for each ear must be used to filter an input signal, ensuring that the outputs are correctly reproduced over their corresponding headphone channel. This is usually done for each separate source signal with the aim of positioning an auditory object in the direction from which the HRTFs have been measured. However, in our proposed approach, the synthesis stage differs significantly from this conventional processing due to the fact that no separate source signals are available. Again, taking into account source sparseness in the time-frequency domain, we will be able to reproduce the original spatial characteristics of the recorded sound scene using the directional information extracted from the previous analysis stage.
4.1. Time-Frequency Selective HRTF Filtering
sound objects are frequently localized inside the head,
frontal sounds often appear behind the listener and vice versa,
the perceived directions of the synthesized sources do not match the intended spatial positions.
These classical problems associated to binaural reproduction have already been extensively studied  and we will not address them in this paper.
Assuming far field conditions, the HRTF is a function of the arrival direction of the source and the frequency , expressed as . Moreover, there is also a different HRTF for the right and left ears, having and .
where and are the STFT of the output signals corresponding to the left and right ears, respectively. These signals are transformed back to the time domain using the inverse STFT operator following an overlap-add scheme.
Using the above approach, the microphone signal provides a pressure signal for the synthesis of the binaural signal. The required spatial localization cues are then given by the HRTF coefficients, which are carefully selected based on the estimated directional data. Note that we only use a single omnidirectional signal for the calculation of the output, since combinations of the microphone signals could result in coloration due to spatial filtering effects. In our implementation, we chose the signal of microphone 4 for being slightly above from the array center.
4.2. Selective Filtering and Sparsity
Further considerations are needed regarding the above synthesis approach. Note that each time-frequency bin is independently filtered according to its DOA information. As well as in the analysis stage, source sparsity and disjointness form the basis of our synthesis method. Under approximate WDO conditions, only one source has a major contribution on a given time-frequency element. Therefore, it is only necessary to filter each bin according to the direction of the dominant contribution since the energy corresponding to the rest of the sources can be neglected with little error. Obviously, if the number of sources is increased, the error will be higher.
Besides the number of sources, the environment and the type of signals also play a major role in our synthesis method. As explained in Section 3.2, speech and music have different sparsity properties and room reflections spread the energy of the sources both in time and frequency. However this is not a serious problem. Time-frequency points dominated by reverberation will be inherently reproduced from multiple directions, just as suggested by the analysis stage. This way, the higher the variance found in the estimated directions, the higher the sense of envelopment will be perceived. In contrast, a dry room that produces very peaky DOA distributions will result in a synthesized binaural signal where the sources can be clearly perceived from their actual directions. A problem associated with a high degree of reverberation is that artifacts may appear due to the prominent discontinuities in the directional data. These effects can be effectively reduced by smoothing the filtering coefficients along the frequency axis.
4.3. HRTF Spatial Resolution
Use directly the HRTF of the available data bank that is closest to the estimated direction. This would be the simplest approach.
Interpolate the available HRTFs to get more accurate filters. This can be done using sophisticated interpolation techniques [36–38]. However, a simple linear interpolation in the time domain using the closest HRIRs has shown to be a very convincing and efficient solution [39, 40].
Depending on the requirements of a given application, a different strategy can be selected. While the interpolation strategy is very useful for achieving accurate localization, the other two methods are computationally more efficient. In the next section we comment on some useful aspects regarding the real-time implementation of the method.
To evaluate subjectively the quality and spatial impression provided by the proposed technique, a set of simulations considering different acoustic situations were carried out. The evaluation conducted this way is useful to assess the performance of the method under different acoustic environments with control on specific aspects of the acoustic setup.
In the experiments, a set of sound sources were simulated inside a shoe-box-shaped room ( m), acquiring all the required impulse responses by means of the Roomsim  simulation package for Matlab. This software simulates the acoustics of a rectangular room by means of the image method  and, moreover, it allows to generate binaural room impulse responses (BRIRs) corresponding to a selected HRTF data set.
A set of 2 male and 2 female speech sources extracted from the public data provided in The 2008 Signal Separation Evaluation Campaign . They are sampled at 16 kHz, 16 bits, and have a duration of 10 s.
A multitrack folk music recording consisting of four instruments: accordion, sax, guitar, and violin. Although originally sampled at 44.1 kHz, they were resampled to have the same sampling frequency (16 kHz) as the above speech mixtures.
The STFT was computed using Hann windows of 1024 samples of length, with a hop size of 512 samples (50% overlap). These parameters have been shown to be optimum for sparsity-based speech processing . However, music would benefit from longer time windows.
A set of 7 listeners took part on an informal listening test with the aim of evaluating the similarities between the scenes rendered by means of the simulated KEMAR and those obtained by means of the proposed approach. The assessed sound scenes were mixtures of one, two, three, and four sources. There were three versions of each scene. Each version was obtained using different room surface characteristics, thus, having different reverberation times: s (anechoic), s (slightly reverberant), and (very reverberant). As a result, there were 2 versions (KEMAR-simulation and proposed) of a total of 24 different sound scenes (12 for speech and 12 for music).
−1: Slightly Worse,
−2: Moderately Worse,
−3: Substantially Worse.
From the above results, it becomes clear that both source overlapping and reverberation affect negatively the performance of the proposed approach. Obviously, this degradation is due to the fact that some of the assumptions taken for the development of the algorithm are not completely met, specially those based on source sparsity and disjointness. A detailed analysis of the artifacts caused by different types of errors in the analysis and synthesis stages could be useful to improve the performance of the method when working in difficult acoustic environments. Although this analysis is out of the scope of this paper, the authors plan to address this issue in future works.
6.1. Real-Time Implementation
In the last section, a set of experiments using simulations of reverberant rooms were presented. Besides considering these simulations, the applicability of the proposed method can be substantially enhanced by providing some notes on the real-time implementation of a working prototype. Two objectives are pursued with this implementation. First, to demonstrate that the computational cost of this technique is reduced enough to be implemented in a practical embedded system. Second, having a real-time system allowed us to plan future interactive experiments where conditions related to scene changes can be experienced as they occur.
For our real-time prototype we used a PC running Microsoft Windows XP as a base. To construct the microphone array prototype with cm, four instrumentation quality microphones from Brüel & Kjaer model 4958 were used. These microphones have excellent phase matching in the audio band. The signal acquiring system consisted of a digital audio interface with four microphone inputs (M-Audio Fast Track Ultra USB 2.0) and ASIO drivers. The Intel Integrated Performance Primitives (Intel IPP)  library was used for FFT computation and vector operations. In the analysis stage, phase differences are calculated from the FFT coefficients of each input data frame at each channel. The components of the DOA vector are then calculated using (13), taking into account that the corresponding frequencies have to be previously stored. Moreover, the processing parameters were set the same as in Section 5.
Since the experiments reported in the following subsection were conducted using a Brüel & Kjaer 4128 Head And Torso Simulator (HATS), the HRTF database used for the synthesis was specifically measured by the authors to allow for an objective comparison. The HRTFs were measured using the logarithmic sweep method  with sampling frequency 44.1 kHz. Moreover, the measuring system was carefully compensated. HRTFs were sampled both in azimuth and elevation. The dummy-head was placed in a rotating table, measuring responses from to every 5 degrees. On the other hand, elevations were measured from to every 10 degrees. For every measure, the same loudspeaker distance to the center was employed (1 m).
6.2. Evaluation and Discussion
In this paper, we have presented a two-step binaural sound synthesis method based on sparse signal processing and time-frequency analysis. In the first stage, the assumption of sound sources that rarely overlap in the time-frequency domain has been considered to study the spatial properties of the sound that impinges a small tetrahedral microphone array. The phase difference information of several microphone pairs is combined to obtain a 3D DOA estimate in each time-frequency slot. In the synthesis stage, one of the microphone signals is selectively filtered in the time-frequency domain with the left and right HRTFs that correspond to the estimated DOAs.
Experiments using both synthetic and real mixtures of speech and music were conducted using different number of sources. Although the performance of the method is slightly degraded with the number of sources and reverberation, the perceived sound quality and spatial impression are considerably similar to conventional binaural reproduction. However, artifacts due to spectral overlapping makes this method more suitable for speech applications than for music.
The proposed spatial sound capturing method not only eliminates the need for an acoustic mannequin, which has a considerable volume and uncomfortable portability, but also allows to change easily the head response by using a different HRTF database in requirement of the application, needs, or user preferences. Moreover, it allows to rotate the head position in real time. Thus, a tracking system can be used to follow the position of the subject in the synthesis stage, providing the listener with a more immersive sensation.
The Spanish Ministry of Science and Innovation supported this work under the Project TEC2009-14414-C03-01.
- Blauert J: Spatial Hearing. MIT Press, Cambridge, UK; 1997.Google Scholar
- Rumsey F: Spatial Audio. Focal Press; 2001.Google Scholar
- Cheng CI, Wakefield GH: Introduction to head-related transfer functions (HRTF's): representations of HRTF's in time, frequency, and space. Proceedings of the 107th Convention of the Audio Engineering Society (AES '99), 1999, New York, NY, USAGoogle Scholar
- Chanda PS, Park S, Kang TI: A binaural synthesis with multiple sound sources based on spatial features of head-related transfer functions. Proceedings of the International Joint Conference on Neural Networks, July 2006, Vacouver, Canada 1726-1730.Google Scholar
- Georgiou PG, Kyriakakis C: A multiple input single output model for rendering virtual sound sources in real time. Proceedings of the IEEE Internatinal Conference on Multimedia and Expo (ICME '00), July 2000, New York, NY, USA 1: 253-256.View ArticleGoogle Scholar
- Breebaart J, Nater F, Kohlrausch A: Parametric binaural synthesis: background, applications and standards. Proceedings of the NAG-DAGA, 2009, Rotterdam, The Netherlands 172-175.Google Scholar
- Breebaart J, Faller C: Spatial Audio Processing: MPEG Surround and Other Applications. Wiley, Chichester, UK; 2007.View ArticleGoogle Scholar
- Breebaart J, Villemoes L, Kjörling K: Binaural rendering in MPEG surround. EURASIP Journal on Advances in Signal Processing 2008, 2008:-14.Google Scholar
- Pulkki V: Spatial sound reproduction with directional audio coding. Journal of the Audio Engineering Society 2007, 55(6):503-516.Google Scholar
- Del Galdo G, Kuech F: Nested microphone array processing for parameter estimation in directional audio coding. Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA '09), October 2009, New Paltz, NY, USAGoogle Scholar
- Pulkki V: Directional audio coding in spatial sound reproduction and stereo upmixing. Proceedings of the AES 28th International Conference, July 2006, Pitea, SwedenGoogle Scholar
- Laitinen M, Pulkki V: Binaural reproduction for directional audio coding. Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA '09), October 2009, New Paltz, NY, USA 337-340.Google Scholar
- Thiergart O, Del Galdo G, Taseska M, Pineda J, Kuech F: In situ microphone array calibration for parameter estimation in directional audio coding. Proceedings of the AES 128th Convention, May 2010, London, UKGoogle Scholar
- Pulkki V: Spatial sound generation and perception by amplitude panning techniques. Helsinki University of Technology, Helsinki, Finland; 2001.Google Scholar
- Laitinen M: Binaural reproduction for directional audio coding, M.S. thesis. Helsinki University of Technology, Helsinki, Finland; 2008.Google Scholar
- Ahonen J, Pulkki V, Lokki T: Teleconference application and B-format microphone array for directional audio coding. Proceedings of the AES 30th International Conference, March 2007, Saariselkä, FinlandGoogle Scholar
- Schultz-Amling R, Kuech F, Kallinger M, Del Galdo G, Ahonen J, Pulkki V: Planar microphone array processing for the analysis and reproduction of spatial audio using directional audio coding. Proceedings of the AES 124th Convention, May 2008, Amsterdam, The NetherlandsGoogle Scholar
- Merimaa J: Applications of a 3-d microphone array. Proceedings of the AES 112th Convention, May 2002, Munich, GermanyGoogle Scholar
- Cohen L: Time-Frequency Analysis. Prentice-Hall; 1995.Google Scholar
- Burred JJ, Sikora T: On the use of auditory representations for sparsity-based sound source separation. Proceedings of the 5th International Conference on Information, Communications and Signal Processing (ICICS '05), December 2005, Bangkok, Thailand 1466-1470.Google Scholar
- Araki S, Sawada H, Mukai R, Makino S: Performance evaluation of sparse source separation and DOA estimation with observation vector clustering in reverberant environments. Proceedings of the International Workshop on Acoustic Echo and Noise Control(IWAENC '06), 2006, Paris, FranceGoogle Scholar
- Rickard S, Dietrich F: DOA estimation of many w-disjoint orthogonal sources from two mixtures using DUET. Proceedings of the 10th IEEE Workshop on Statistical Signal and Array Processing (SSAP '00), August 2000, Pocono Manor, Pa, USA 311-314.Google Scholar
- Yilmaz Ö, Rickard S: Blind separation of speech mixtures via time-frequency masking. IEEE Transactions on Signal Processing 2004, 52(7):1830-1847. 10.1109/TSP.2004.828896MathSciNetView ArticleGoogle Scholar
- Bofill P, Zibulevsky M: Underdetermined blind source separation using sparse representations. Signal Processing 2001, 81(11):2353-2362. 10.1016/S0165-1684(01)00120-7View ArticleMATHGoogle Scholar
- Rickard S, Yilmaz O: On the w-disjoint orthogonality of speech. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2002, Orlando, Fla, USA 529-532.Google Scholar
- Jourjine A, Richard S, Yilmaz O: Blind separation of disjoint orthogonal signals: demixing n sources from 2 mixtures. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '00), 2000, Istanbul, Turkey 5: 2985-2988.Google Scholar
- Burred JJ: From sparse models to timbre learning: new methods for musical source separation, Ph.D. thesis. Technical University of Berlin; 2008.Google Scholar
- Schulz S, Herfet T: On the window-disjoint-orthogonality of speech sources in reverberant humanoid scenarios. Proceedings of the 11th International Conference on Digital Audio Effects (DAFx '08), September 2008, Espoo, FinlandGoogle Scholar
- Cobos M, Lopez JJ: Two-microphone separation of speech mixtures based on interclass variance maximization. Journal of the Acoustical Society of America 2010, 127(3):1661-1672. 10.1121/1.3294713View ArticleGoogle Scholar
- Cobos M, Lopez JJ, Spors S: Effects of room reverberation in source localization using small microphone arrays. Proceedings of the 4th International Symposium on Communications, Control and Signal Processing (ISCCSP '10), March 2010, Limassol, CyprusGoogle Scholar
- Algazi VR, Duda RO, Thompson DM, Avendano C: The CIPIC HRTF database. Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, October 2001, New Paltz, NY, USA 99-102.Google Scholar
- IRCAM : LISTEN HRTF database. 2003, http://recherche.ircam.fr/equipes/salles/listen/
- Brown CP, Duda RO: An efficient HRTF model for 3-D sound. Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (ASSP '97), 1997Google Scholar
- Møller H, Sørensen MF, Jensen CB, Hammershøi D: Binaural technique: do we need individual recordings? Journal of the Audio Engineering Society 1996, 44(6):451-468.Google Scholar
- Enzner G: 3-d-continuous-azimuth acquisition of head-related impulse responses using multi-channel adaptive filtering. Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA '09), October 2009, New Paltz, NY, USA 325-328.Google Scholar
- Keyrouz F, Diepold K: Efficient state-space rational interpolation of HRTFs. Proceedings of the AES 28th International Conference, 2006, Pitea, SwedenGoogle Scholar
- Freeland F, Biscainho L, Diniz P: Efficient HRTF interpolation in 3D moving sound. Proceedings of the AES 22nd International Conference on Virtual, Synthetic and Entertainment Audio, June 2002, Espoo, FinlandGoogle Scholar
- Keyrouz F, Diepold K: A new HRTF interpolation approach for fast synthesis of dynamic environmental interaction. Journal of the Audio Engineering Society 2008, 56(1-2):28-35.Google Scholar
- Sodnik J, Sušnik R, Štular M, Tomažič S: Spatial sound resolution of an interpolated HRIR library. Applied Acoustics 2005, 66(11):1219-1234. 10.1016/j.apacoust.2005.04.003View ArticleGoogle Scholar
- Nishino T, Mase S, Kajita S, Takeda K, Itakura F: Interpolating HRTF for auditory virtual reality. Journal of the AcousticalSociety of America 1996, 100(4):2602-2602.View ArticleGoogle Scholar
- Algazi V, Duda R, Thomson DM: The use of head-and-torso models for improved spatial sound synthesis. Proceedings of the AES 113th Convention, October 2002, Los Angeles, Calif, USAGoogle Scholar
- Campbell DR: Roomsim: a MATLAB simulation shoebox room acoustics. 2007, http://media.paisley.ac.uk/~campbell/Roomsim/Google Scholar
- Allen JB, Berkley DA: Image method for efficiently simulating small-room acoustics. Journal of the Acoustical Society of America 1979, 65(4):943-950. 10.1121/1.382599View ArticleGoogle Scholar
- Gardner B, Martin K: HRTF measurements of a KEMAR dummy-head microphone. MIT Media Lab, May 1994, http://alumni.media.mit.edu/~kdm/hrtfdoc/hrtfdoc.htmlGoogle Scholar
- Vincent E, Araki S, Bofill P: The 2008 signal separation evaluation campaign: a community-based approach to large-scale evaluation. Independent Component Analysis and Signal Separation, Lecture Notes in Computer Science 2009, 5441: 734-741. 10.1007/978-3-642-00599-2_92View ArticleGoogle Scholar
- Taylor S: Intel Integrated Performance Primitives. Intel Press; 2004.Google Scholar
- Müller S, Massarani P: Transfer-function measurement with sweeps. Journal of the Audio Engineering Society 2001, 49(6):443-471.Google Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.