Significance-aware filtering for nonlinear acoustic echo cancellation
- Christian Hofmann^{1}Email author,
- Christian Huemmer^{1},
- Michael Guenther^{1} and
- Walter Kellermann^{1}
DOI: 10.1186/s13634-016-0410-7
© The Author(s) 2016
Received: 26 June 2016
Accepted: 6 October 2016
Published: 8 November 2016
Abstract
This article summarizes and extends the recently proposed concept of Significance-Aware (SA) filtering for nonlinear acoustic echo cancellation. The core idea of SA filtering is to decompose the estimation of the nonlinear echo path into beneficially interacting subsystems, each of which can be adapted with high computational efficiency. The previously proposed SA Hammerstein Group Models (SA-HGMs) decompose the nonlinear acoustic echo path into a direct-path part, modeled by a Hammerstein Group Model (HGM) and a complementary part, modeled by a very efficient Hammerstein model. In this article, we furthermore propose a novel Equalization-based SA (ESA) structure, where the echo path is equalized by a linear filter to allow for an estimation of the loudspeaker nonlinearities by very small and efficient models. Additionally, we provide a novel in-depth analysis of the computational complexity of the previously proposed SA and the novel ESA filters and compare both SA filtering approaches to each other, to adaptive HGMs, and to linear filters, where fast partitioned-block frequency-domain realizations of the competing filter structures are considered. Finally, the echo reduction performance of the proposed SA filtering approaches is verified using real recordings from a commercially available smartphone. Beyond the scope of previous publications on SA-HGMs, the ability of the SA filters to generalize for double-talk situations is explicitly considered as well. The low complexity as well as the good echo reduction performance of both SA filters illustrate the potential of SA filtering in practice.
Keywords
Significance-aware Equalization-based significance-aware Efficiency Nonlinear acoustic echo cancellation Hammerstein group model Hammerstein model1 Introduction
Since the first adaptive linear echo canceler for network echoes in telephone lines [1], linear echo cancellation has evolved to a key ingredient of almost any full-duplex speech communication system. This has resulted in a multitude of approaches to efficiently model, parametrize, and estimate even complex linear systems, such as the acoustic echo paths in hands-free wideband telecommunication scenarios [2]. With increasingly common nonlinear distortions produced by miniaturized amplifiers and loudspeakers in modern portable devices, dedicated nonlinear echo path models have emerged as an important topic of research and motivated sophisticated approaches for nonlinear Acoustic Echo Cancellation (AEC) based on Volterra filters [3–5], artificial neural networks [6, 7], Functional Link Adaptive Filters (FLAFs) [8, 9], or kernel methods [10, 11]. A very simple, yet effective model for nonlinear acoustic echo paths is the cascade of a memoryless preprocessor (modeling loudspeaker signal distortions) and a subsequent linear system (modeling sound propagation through air) [12]. Due to its simplicity, this so-called Hammerstein Model (HM) has been frequently employed [13–22] and will also be used in this contribution. So will be a group of B parallel HMs, referred to as Hammerstein Group Model (HGM) in the following. The recently proposed efficient Significance-Aware HGM (SA-HGM) [20] combines the advantages of HMs and HGMs and was extended to an efficient partitioned-block frequency-domain realization in [22].
Beyond previous work, this article introduces a novel variant of the Significance-Aware (SA) filtering concept denoted as Equalization-based SA (ESA) filtering which complements the existing efficient frequency-domain realization in [22]. Thereby, highly efficient SA filters can be derived for higher-order nonlinear systems—even without block partitioning. Furthermore, a novel in-depth analysis of the computational complexity of the previously proposed SA filtering concepts is provided and contrasted with the computational complexity of the novel ESA filtering concept, adaptive HGMs, and conventional adaptive linear filters. Beyond previous investigations [22], this article also assesses the ability of the considered echo path models to generalize for double-talk situations, in which the models cannot be adapted to the current input signal. The performance in such situations reflects how well the estimated system models the physical system to be identified.
The remainder of this paper is structured as follows: after introducing the notation in Section 2, frequently used echo path models and their adaptation are reviewed in Sections 3.1 and 3.2, respectively. Afterwards, the recently proposed Partitioned-Block SA-HGM (PBSA-HGM) is summarized in Section 4.1, before introducing the novel ESA filtering concept in Section 4.2, which results in an Equalization-based Significance-Aware HM (ESA-HM). Then, the computational complexity of the SA filters (PBSA-HGM and ESA-HM) is analyzed and compared to the complexity for adapting a linear model and an HGM in Section 5.1. An experimental verification of the efficacy of the novel ESA-HM in comparison to other approaches in terms of echo reduction performance is given in Section 5.2. Finally, the manuscript is complemented by a summary of the main results in Section 6.
2 Notation
setting the first or second half of a length-2M vector to zero, respectively. Furthermore, F and F ^{H} denote the Nth-order Discrete Fourier Transform (DFT) matrix and its inverse, respectively. Besides, a(k)∗b(k) and \(a(k)\circledast b(k)\) denote linear and cyclic convolution between time series a(k) and b(k), respectively, where k is the discrete-time sample index. Analogously to vectors, \(\left \langle a(k),b(k)\right \rangle =\sum _{k=-\infty }^{\infty }a(k)b^{*}(k)\) denotes the scalar product between the time series a(k) and b(k). For a real-valued scalar a, ⌈a⌉ refers to the smallest integer number larger than or equal to a, also known as ceiling function.
3 Fundamentals of linear and nonlinear acoustic echo cancellation
Therein, the discrete-time loudspeaker signal x(k), where k is the sample index, is interpolated by the D/A converter, amplified, and played-back via a loudspeaker, yielding the analog loudspeaker signal x _{a}(t), where t is the continuous time variable. The subsequent propagation of the sound waves through the medium air to the microphone can be modeled very accurately by a linear system with impulse response h _{a}(t). These acoustic echoes (far-end components) superimpose with local speakers and interferences (near-end components), evoking the continuous-time microphone signal y _{a}(t) and its sampled, discrete-time representation y(k). In order to provide good estimates of local (near-end) speakers, two technologies are typically employed: an AEC unit for removing the far-end signal components from y(k) and a postfilter suppressing residual echoes and near-end interferences [23–32].
3.1 From linear to nonlinear echo path models
This section contains a brief overview of frequently employed echo path models. Strategies for estimating the model parameters (i.e., filter coefficients) will be described in Section 3.2.
3.1.1 Linear models
where h(k) is the discrete-time impulse response of the LEMS. Such models are most suitable for high-quality audio equipment. On the other hand, energy-efficient and miniaturized portable devices operating at the limit of their capabilities (such as portable navigation devices or smartphones in hands-free mode) lead to nonlinear distortions in the played-back signal [12] (see red box in Fig. 1), which render linear echo path models insufficient.
3.1.2 Hammerstein models
with nonlinearity basis functions f _{ b }{·} and preprocessor weights (parameters) w _{ b }, which corresponds to the block diagram in Fig. 2 c. Considering a Bth-order polynomial preprocessor, intermodulation products up to the order B may be modeled. Examples for such polynomial preprocessors can be found in [13, 14].
3.1.3 Hammerstein group models
where x _{ b } (k) will be referred to as branch signals in the following.
Practical examples for such HGMs employ, e.g., monomes as nonlinearity basis functions [44, 45] (so-called power filters) and Legendre polynomials [19–22], as well as sinusoidal functions [8, 9, 42]. Some of the publications employing sinusoidal functions also refer to HGMs as FLAF without memory [8, 9]. Inspired by machine learning, FLAFs typically express the input/output relation of (5) differently: instead of individual branch signals, all branch signals are interleaved and concatenated to a larger expanded vector, which is mapped to an echo estimate by linear combination (covers both convolution and summation in (5)). However, with focus on the applicability of fast convolution methods in this article, the formulation according to a group of parallel Hammerstein systems is advantageous.
This correspondence will be essential for the SA filters in Section 4, where b _{ref}=1 (implying w _{1}=1) will be considered by default.
3.1.4 Models with dynamic nonlinearities
A further generalization of HGMs are models, where the nonlinearity basis functions are nonlinear functions f _{ b }{x(k)} depending on a vector x (k), which is formed from samples of the input signal x (k). The most prominent example for such filters are the so-called Volterra filters [3–5, 40, 46–48], for which f _{ b }{x(k)} computes time-lagged products of samples of x(k) (different elements of x(k))^{1}. Note that power filters [44, 45] (having a memoryless nonlinearity) represent the special case of the Volterra filters where only the main diagonal of each Volterra kernel is populated with non-zero coefficients, corresponding to the respective kernels of the power filter. Volterra filters can be seen as a multidimensional Taylor series expansion of the function x(k)↦y(k). Alternatively, the monomial basis functions of Volterra filters have also been replaced by Legendre polynomials [49, 50] or Fourier basis functions (sinusoids) [51]. Note that for the Fourier-basis nonlinear filters, the feed-forward structure has also been complemented by a feedback path, leading to bounded-input/bounded-output (BIBO)-stable recursive nonlinear filters [52].
Although the SA filters described in Section 4 will employ only HGMs and memoryless preprocessors, it is worth noticing that the SA filtering concept can also be applied to dynamic nonlinearities f _{ b }{x(k)}, involving, e.g., Volterra filters instead of HGMs.
3.2 Adaptation strategies
Adaptation of nonlinear models can be performed on different levels. On a basic level, parameters like filter coefficients of a model with a given structure are identified. To this end, the parameters may be modeled as deterministic parameters, resulting in, e.g., Least-Mean-Square (LMS) algorithms [53] for adaptation, or as probabilistic parameters, leading to Kalman filter-like algorithms [15, 19, 21, 54]. On a higher level, the model structure (e.g., filter lengths of linear subsystems or numbers of diagonals of Volterra kernels) can be estimated via self-configuring evolutionary algorithms [5, 55–57]. In this article, the term “adaptation” will refer to the iterative estimation of filter coefficients modeled as deterministic parameters of a model with fixed structure.
In this section, the adaptation of the linear subsystems of the models described in Section 3.1 will be discussed. As all these models can be expressed as special cases of a parallel structure like an HGM, the adaptation will be described for an HGM. Then, the adaptation schemes for all other models can be derived as special cases of the HGM case. In particular, two common approaches for filter adaptation will be revisited: the direct adaptation of the impulse responses in the time domain by a Normalized Least-Mean-Square (NLMS) algorithm and the adaptation of partitioned versions of the impulse responses in the frequency domain, which allows a smooth trade-off between computational efficiency and algorithmic delay (latency) of the digital signal processing system.
3.2.1 Time-domain adaptive filtering
A straightforward identification of the linear filters of an HGM is possible by iteratively minimizing a quadratic cost function derived from the error signal e (k). Most common algorithms are LMS-, affine projection-, or Recursive Least-Squares (RLS)-type algorithms [53]. Due to its computational efficiency, the adaptation of an HGM by an NLMS algorithm, which aims at minimizing the Mean Squared Error (MSE) in a gradient-descent manner, will be considered. The following description is independent of the actual choice of the nonlinearity basis functions f _{ b }{·}, as long as f _{ b }{·} are time-invariant (Legendre polynomials will be employed for the evaluations in Section 5).
This allows to adaptively identify the individual branches of HGMs but also covers the identification of single HMs and linear models as special cases.
3.2.2 Partitioned-block frequency-domain adaptive filtering
3.2.2.1 Partitioned-block convolution:
A linear convolution can be realized despite large filter lengths L with a low input/output delay efficiently by block-based processing methods like Partitioned-Block Frequency-Domain Filtering (PBFDF) [56, 58–60], also known as multidelay convolution [61]. In the following, only a uniform partitioning with frame shift M and frame size N=2M will be considered.
where \(P =\left \lceil \frac {L}{M}\right \rceil \) is the number of non-zero impulse response partitions and where the windowing matrix W _{01} according to (1) suppresses additionally computed samples in y°(κ), which result from the previous frame and may contain cyclic convolution artifacts.
Note that such a PBFDF scheme is computationally efficient because each input signal frame’s DFT has to be computed only once.
3.2.2.2 Partitioned-block frequency-domain adaptive filtering (PBFDAF):
This is equivalent to the classical formulation of the so-called constrained update, where the constraint (zeros in time domain) is imposed on the update (see [61]). Yet, the computation of \({\hat {\mathbf {h}}_{b}^{(p)}({\kappa })}\) as a byproduct of the filter constraint in (28) will be beneficial for the SA filtering later on.
As for time-domain adaptive filtering, the algorithm for adapting an HGM in the partitioned-block frequency domain covers the adaptation of an HM (an HGM with B=1 and f _{1}{x(k)}=f{x(k)}) and a linear model (an HGM with B=1 and x _{1}(k)=f _{1}{x(k)}=f{x(k)}=x(k)). Furthermore, the non-partitioned FNLMS algorithm, derived in many textbooks, e.g., [53, 62], results from the aforementioned partitioned-block description for P=1 with sufficiently large N=2L. Adapting a model with the block partitioning will be referred to as Partitioned-Block Frequency-domain Normalized Least Mean Squares (PB-FNLMS) algorithm.
4 Significance-aware filtering
Significance-aware (SA) filtering is a generalized system identification concept which exploits prior knowledge about the physical system to be identified by decomposing the adaptive model in a divide-and-conquer manner into beneficially interacting subsystems.
and combines the branch signals x _{ b }(k) with frame-wise updated weights \(\hat {w}_{b} (\kappa)\). These weights \(\hat {w}_{b} (\kappa)\) are estimated from the kernels of the HGM subsystem (will be explained in Section 4.1.3). Thereby, a nonlinear model estimated from the direct path is extrapolated to the entire acoustic echo path.
These two features, the decomposition and the preprocessor coefficient estimation via an HGM, are key components of both the recently proposed PBSA-HGM [22], revisited in Section 4.1, and the novel alternative SA filtering structure, which will be denoted by ESA filtering and will be introduced in Section 4.2.
4.1 Significance-aware Hammerstein group models
SA-HGMs are efficient because the potentially computationally expensive HGM only has to model a small temporal support of the acoustic echo path. To realize this concept while exploiting the benefit of fast frequency-domain convolution, a partitioned-block variant of the SA-HGMs, denoted as PBSA-HGM, has recently been introduced in [22]^{3}. This PBSA-HGM will be briefly reviewed in the following. Employing the uniform partitioning according to Section 3.2.2 leads to an adaptive HM with DFT-domain partition estimates \(\hat {\underline {\mathbf {h}}}^{(p)}({\kappa })\) for partitions p=0,…,P−1 and an adaptive HGM with DFT-domain estimates \(\hat {\underline {\mathbf {h}}}_{b}^{({p}_{\mathrm {d}})}(\kappa)\) for branches b=1,…,B but just a single partition with index p _{d}. This partition with index p _{d} should capture the direct path and thus a significant portion of the energy transmitted from the loudspeaker to the microphone. Note that \(\hat {\underline {\mathbf {h}}}^{(p_{\mathrm {d}})}{(\kappa)}\) models h _{d}(k) in Fig. 5 and \(\hat {\underline {\mathbf {h}}}^{(p)}{(\kappa)}\forall p \neq {p}_{\mathrm {d}}\) jointly model h _{c}(k) in Fig. 5. In the following, the estimation of \(\hat {\underline {\mathbf {h}}}^{(p)}{(\kappa)}\) (covering both h _{d}(k) and h _{c}(k) of Fig. 5) and \(\hat {\underline {\mathbf {h}}}_{b}^{(p_{\text {d}})}{(\kappa)}\) will be described in Sections 4.1.1 and 4.1.2, respectively, and the estimation of the HM’s preprocessor coefficients \(\hat {w}_{b} (\kappa)\) based on \(\hat {\mathbf {\underline {h}}}_{b}^{({p_{\mathrm {d}}})}({\kappa })\) will be explained in Section 4.1.3.
4.1.1 Estimation of the RIR of the HM submodel
where \(\underline {\mathbf {x}}_{\text {pp}}{(\kappa)}= \mathbf {F}\mathbf {x}_{\text {pp}}{(\kappa)}\) with x _{pp}(κ) from (30).
where \(\underline {\mathbf {x}}_{\mathrm {norm,}\text {pp}}{(\kappa -p)}\) is the normalized DFT-domain signal vector computed analogously to (26) from the preprocessed input \(\underline {\mathbf {x}}_{\text {pp}}{(\kappa)}\).
4.1.2 Estimating the HGM submodel
where \(\underline {\mathbf {x}}_{\text {norm},b}(\kappa)\) is the normalized DFT-domain signal vector computed analogously to (26). The reason for the application of the windowing in (37) to the actual impulse response partitions will become obvious in the following section.
4.1.3 Estimating the preprocessor of the HM
where 0≤γ _{w}<1. In the following, γ _{ w }=0.95 will be employed per default. Note that \(\hat {w}_{1}{(\kappa)}=1\,\forall \kappa \) and therefore does not need to be computed at all. As in [20, 22], the first branch (b=1) will be assumed to be linear (f _{1}{x(t)}=x(t)), such that an entirely linear echo path model results from a preprocessor with \(\hat {w}_{b}({\kappa })=0\,\,\forall \,b>1\).
By the method described in Section 4.1 and its subsections, the estimation of the nonlinear system has been split into two beneficially interacting subproblems (HM and HGM adaptation). The beneficial interaction is achieved by the preprocessor coefficient refinement based on the HGM and by the extension of the temporal support of the HGM employing partitions of the HM. An in-depth evaluation of the computational complexity of such a PBSA-HGM will be given in Section 5.1.
4.2 Equalization-based significance-aware Hammerstein models
Previous applications of the SA concept [20–22] split the echo path model along the time axis (using knowledge about a dominating direct-path component), to allow for an efficient estimation of nonlinear parameters (see also Section 4.1). This corresponded to a parallel decomposition of the acoustic echo path (see h _{c}(k) and h _{d}(k) of the HM submodel block in Fig. 5, where f _{pp}{·} can be shifted in the parallel branches of h _{c}(k) and h _{d}(k) as well).
4.2.1 Equalization of the RIR (block γ)
On the one hand, (41) suggests that \(\hat {x}_{\text {NL}}(k -L)\) is an estimate for x(k−L), obtained via linear filtering of y(k). On the other hand, the linear equalizer \(\hat {h}_{\text {eq},l} (k)\) cannot equalize the nonlinear components of the LEMS, such that the nonlinear distortion remains in the equalizer output and \(\hat {x}_{\text {NL}}(k - L)\) according to (41) can be seen as an estimate for the nonlinearly distorted signal x _{NL}(k−L). Thus, while the inability of impulse responses to model nonlinear systems hampers the performance of the AEC system in the first place, this inability is exploited here to estimate an otherwise inaccessible intermediate signal to guide the estimation of the nonlinear components (described in the next paragraphs). Furthermore, the equalization is only as complex as a single linear AEC system (assuming identical filter lengths).
4.2.2 Estimating the nonlinearity (block β)
Due to the small number of modeled taps L _{SA}, such a time-domain algorithm is computationally inexpensive.
4.2.3 Estimating the preprocessor of the HM (between blocks β and α)
where 0≤γ _{w}<1.
4.2.4 Computation of the echo estimate
The overall computational complexity of this algorithm is slightly higher than two adaptive linear filters (originating from blocks α and γ). A detailed analysis of the computational complexity of this novel algorithm and of previously proposed algorithms will be given in the following in Section 5.1.
4.3 Structural comparison of SA and ESA filtering
In the following, the similarities and differences between the previously proposed SA filtering and the novel ESA filtering will be summarized. Note that in this section, the notation of time-domain filters will be employed, despite their potential realization as block-partitioned adaptive filters in the frequency domain.
Similarities Both the SA-HGM and ESA-HM decompose the acoustic echo path into subsystems to facilitate the efficient estimation of an HGM to estimate the nonlinear components of the echo path. Furthermore, both the SA-HGM and ESA-HM estimate the preprocessor coefficients of an HM from identified HGM kernels as a least squares estimate and use this preprocessor in an HM with a long subsequent impulse response. Therefore, both structures are considered as different variants of SA filtering.
5 Evaluation
In this section, the novel and the previously proposed SA filtering concepts will be compared to each other and to classical adaptive filters in terms of computational complexity (Section 5.1) and echo cancellation performance (Section 5.2).
5.1 Computational complexity
This section contains an in-depth analysis of the computational complexity of the previously discussed adaptive filters. In practice, the actual computational load of an algorithm is determined by many platform-specific factors, such as the instruction set, the number of clock cycles for a particular arithmetic operation, pipelining, and caching abilities of the processor. Furthermore, especially with growing amount of data to be processed, the memory access pattern may significantly impact how well the processor exploits its capabilities or waits for new data from external memory. Nevertheless, the number of FLoating Point Operations (FLOPs) is still a commonly accepted indicator for the computational complexity of an algorithm on modern platforms, where the different arithmetic operations require similar execution time in principle. Operations counted as FLOPs will be real-valued multiplications (RMULs), additions (RADDs), and divisions (RDIVs).
Computational effort for the identification of an HGM by a time-domain NLMS algorithm
Computed quantity | Equation | Multiplicity | Required operations | |||||
---|---|---|---|---|---|---|---|---|
FFT | CMUL | CADD | RMUL | RADD | RDIV | |||
Branch signals | (7) | ∀b | n/a | n/a | n/a | n/a | n/a | n/a |
Output signal | (8) | 0 | 0 | 0 | BL | B(L−1) | 0 | |
Error signal | (9) | 0 | 0 | 0 | 0 | 1 | 0 | |
Input power | (11) | ∀b | 0 | 0 | 0 | B | 0 | 0 |
Frame energy | (12) | ∀b | 0 | 0 | 0 | 0 | 2B | 0 |
Normalized error | (13) | ∀b | 0 | 0 | 0 | B | B | B |
Filter coefficients | (14) | ∀b,l | 0 | 0 | 0 | BL | BL | 0 |
Accumulated: | 0 | 0 | 0 | 2B(L+1) | 2B(L+1)+1 | B | ||
Total: B(4L+5)+1 FLOPs |
The first column contains the name of the computed quantity, the second column contains the corresponding equation number in this article, the third column indicates how often the equation has to be evaluated, and the subsequent columns list the number of FFTs, complex-valued, and real-valued operations for the particular equation. Below this list, the accumulated number of individual operations and the total number of FLOPs are listed as well. Note that these operation counts are given per output sample for this time-domain algorithm. The frequency-domain algorithms considered in the following will be analyzed on a frame-wise basis.
Computational effort for the identification of an HGM by a PB-FNLMS algorithm
Computed quantity | Equation | Multiplicity | Required operations | |||||
---|---|---|---|---|---|---|---|---|
FFT | CMUL | CADD | RMUL | RADD | RDIV | |||
Branch signals | (21) | ∀b | n/a | n/a | n/a | n/a | n/a | n/a |
(22) | ∀b | B | 0 | 0 | 0 | 0 | 0 | |
Output signal | (24) | 0 | B P N _{N} | (B P−1)N _{N} | 0 | 0 | 0 | |
Error signal | (23) | 2 | 0 | 0 | 0 | \(\frac {N}{2}\) | 0 | |
Branch PSDs | (25) | ∀b | 0 | 0 | 0 | 4B N _{N} | 2B N _{N} | 0 |
Normalized branch signals | (26) | ∀b | 0 | 0 | 0 | 2B N _{N} | B N _{N} | B N _{N} |
Filter coefficients | (27) | ∀b,p | 0 | B P N _{N} | B P N _{N} | 0 | 0 | 0 |
Filter constraint | (28) | ∀b,p | 2B P | 0 | 0 | 0 | 0 | 0 |
Accumulated: | B+2B P+2 | 2B P N _{N} | N _{N}(2B P−1) | 6B N _{N} | \({\frac {N}{2}+3BN_{\mathrm {N}}}\) | B N _{N} | ||
Total: \(\left (B\left (4P+2\right)+4\right)N\log _{2}N+B\left (8P+\frac {7N}{2}+5PN +6\right)-\frac {7N}{2}-10\) FLOPs |
Computational effort for the identification of a PBSA-HGM with a PB-FNLMS algorithm
Computed quantity | Equation | Multiplicity | Required operations | |||||
---|---|---|---|---|---|---|---|---|
FFT | CMUL | CADD | RMUL | RADD | RDIV | |||
Preprocessed input | (30) | \(p=0,\dots,\frac {N}{2}-1\) | 0 | 0 | 0 | (B−1)N/2 | (B−1)N/2 | 0 |
HM submodel | Table 2, B=1 | 2P+3 | 2P N _{N} | N _{N}(2P−1) | 6N _{N} | \(\tfrac {N}{2}+3N_{\mathrm {N}}\) | N _{N} | |
HGM submodel | Table 2, P=1 | 3B+2 | 2B N _{N} | N _{N}(2B−1)+N _{N} | 6B N _{N} | \(\frac {N}{2}+3BN_{\mathrm {N}}\) | B N _{N} | |
Kernel correlation | (39) | ∀b | 0 | 0 | 0 | B N/2 | B(N/2−1) | 0 |
Instantaneous weights | (38) | ∀b>1 | 0 | 0 | 0 | 0 | 0 | B−1 |
Smoothing weights | (40) | ∀b>1 | 0 | 0 | 0 | 2(B−1) | B−1 | 0 |
Accumulated: | 3B+2P+5 | 2N _{N}·(B+P) | N _{N}(2B+2P−1) | \(\begin {aligned} 2B-\frac {N}{2}+\\ 6N_{\mathrm {N}}+BN+\\6BN_{\mathrm {N}}-2 \end {aligned}\) | \(\begin {aligned} \frac {N}{2}+3N_{\mathrm {N}}+\\ BN+\\3BN_{\mathrm {N}}-1 \end {aligned}\) | B+N _{N}+B N _{N}−1 | ||
Total: \(\left (6B+4P+10\right)N\log _{2}N+8P-\frac {7N}{2}+B\left (\frac {21N}{2}+17\right)+5PN-16\) FLOPs |
Computational effort for the identification of a ESA-HM with a PB-FNLMS algorithm
Computed quantity | Equation | Multiplicity | Required operations | |||||
---|---|---|---|---|---|---|---|---|
FFT | CMUL | CADD | RMUL | RADD | RDIV | |||
Preprocessed input | (30) | \(p=0,\dots,\frac {N}{2}-1\) | 0 | 0 | 0 | (B−1)N/2 | (B−1)N/2 | 0 |
HM submodel (forward) | Table 2,B=1 | 2P+3 | 2P N _{N} | N _{N}(2P−1) | 6N _{N} | \(\frac {N}{2}+3N_{\mathrm {N}}\) | N _{N} | |
Inversion submodel | Table 2,B=1 | 2P+3 | 2P N _{N} | N _{N}(2P−1) | 6N _{N} | \(\frac {N}{2}+3N_{\mathrm {N}}\) | N _{N} | |
Time-domain HGM | Table 1,L=L _{SA} | \(\frac {N}{2}\) | 0 | 0 | 0 | \(2B(L_{\text {SA}}+1)\frac {N}{2}\) | \(2B(L_{\text {SA}}+1)\frac {N}{2}+\frac {N}{2}\) | \(B\frac {N}{2}\) |
submodel | ||||||||
Kernel correlation | (46) | ∀b | 0 | 0 | 0 | B L _{SA} | B(L _{SA}−1) | 0 |
Instantaneous weights | (45) | ∀b>1 | 0 | 0 | 0 | 0 | 0 | B−1 |
Smoothing weights | (47) | ∀b>1 | 0 | 0 | 0 | 2(B−1) | B−1 | 0 |
Accumulated: | 4P+6 | 4P N _{N} | N _{N}(4P−1) | \(\begin {aligned} 2B+12N_{\mathrm {N}}+\\\frac {N(B-1)}{2}+BL_{\text {SA}}+\\ BN(L_{\text {SA}}+1)-2 \end {aligned}\) | \(\begin {aligned} N+6N_{\mathrm {N}}+\\BL_{\text {SA}}+\frac {3BN}{2}+\\BL_{\text {SA}} N-1 \end {aligned}\) | \(\begin {aligned} B+\\2N_{\mathrm {N}}+\\\frac {BN}{2}-1 \end {aligned}\) | ||
Total: \(\left (8P+12\right)N\log _{2}N+16P+\frac {N}{2}+\left (2L_{\text {SA}}+\frac {7N}{2}+2L_{\text {SA}} N+3\right)B+10PN-10\) FLOPs |
Obviously, the ESA-HM has the constant offset of another linear filter for system inversion, which makes it unattractive in comparison to the previously proposed PBSA-HGM for only two or three branches. For two branches, even a full PB-HGM is marginally more efficient than an ESA-HM and only slightly more complex than a PBSA-HGM. However, with increasing number of branches, the benefit of an ESA-HM in comparison to a PB-HGM or even to the less complex PBSA-HGM becomes evident: an ESA-HM’s complexity is mainly dominated by the two linear adaptive filters—even for B=20 branches.
Varying the number of frames P and leaving B=5 constant results in the relative FLOPs depicted in Fig. 8 b, where relative FLOPs result from normalization to the FLOPs of an adaptive linear model with a single partition. As can be seen, the ESA-HM leads to a significantly reduced computational complexity for a low number of frames P. Thus, the ESA-HM always leads to a complexity reduction compared to the PB-HGM, even for P=1, which corresponds to conventional frequency-domain adaptive filtering without block partitioning.
5.2 Echo reduction performance
In this section, the echo reduction performance of the novel ESA and the previously proposed SA filtering concept will be compared to classical adaptive filters based on linear models and HGMs.
5.2.1 Experimental setup
Properties of setups A to E for the AEC experiments
Room type | T_{60} | Room dimensions | |
---|---|---|---|
Setup A | Living room | 900 ms | 4 × 5 × 2.5 m |
Setup B | Living room | 900 ms | 4 × 5 × 2.5 m |
Setup C | Anechoic chamber | n/a | 2 × 2 × 2 m |
Setup D | Lab environment with curtains on walls | 250 ms | 5.5 × 5.7 × 3.1 m |
Setup E | Lab environment | 400 ms | 5.5 × 5.7 × 3.1 m |
Setups A and B correspond to recordings with a duration of about 130 s in a living room-like environment. Setups A and B differ in the placement of the smartphone within the same environment. Setup C corresponds to a recording in an anechoic environment and has a duration of about 80 s. The data for setups D and E are synthesized by convolving the anechoic nonlinear recordings obtained from setup C with measured RIRs. The employed RIRs are from a lab environment with variable acoustics, once with curtains at the walls (setup D) and once with open curtains (setup E). For all setups, a partitioned-block linear model (PB-HGM with a single linear branch), a full PB-HGM (full temporal support), a PBSA-HGM, and an ESA-HM will be compared.
In the following experiments, processing is done at a sampling rate of f _{S}=16 kHz. The acoustic echo paths are estimated with FIR filters of length L=1024 taps and the input signals and impulse responses are partitioned into P=4 blocks with a relative frame shift of M=256 taps (implying N=512). Note that, despite large values of T_{60} for some setups, the echo reduction performance in the experiments is not limited by the filter length but by the nonlinearity of the echo path (see Section 5.2.2 for a quantitative analysis of the amount of nonlinearity in the recordings).
As in [20], the HGMs consist of B=5 branches, with odd-order Legendre polynomials up to order 9 as nonlinearity basis functions f _{ b }{·} in (5). The algorithm parameters used in the experiments have been selected based on the recordings of setup C. In particular, a stepsize of μ _{ b }=0.1 (see (26)) is used for the kernels of the PB-HGMs and a stepsize of μ _{ b }=0.2 is used for the PBSA-HGMs and ESA-HMs. For the reference algorithm, consisting of the linear filter, a stepsize of μ _{1}=0.5 is employed to account for the lack of multiple adaptation branches. Furthermore, the adaptation process is refined using the robust statistics described in [34, 59], which hardly affects the computational complexity but robustifies the system identification against outliers in the error signal. For the same purpose, the dynamics of the adaptive preprocessor weights is limited to a maximum stepsize of ±0.001 per frame.
where the numerator is the power of the microphone signal y(k) and the denominator is the power of the error signal e(k) produced by the model to be evaluated. For a practical evaluation, the mathematical expectation (ensemble average) in (51) will be replaced by a time-averaging over the entire sequences. Note that the ERLE measure in its general definition of (50) does not depend on the near-end signal components (including the number and positions of the near-end sources) but requires knowledge of the actual far-end signal component during double-talk periods. This knowledge is not available in practice. Nonetheless, the ERLE performance during double-talk periods can be simulated by performing AEC on single-talk recordings with previously determined fixed filters (as will be done in Section 5.2.3 in experiment 2).
5.2.2 Quantification of nonlinearity
As can be seen, the LNR (f) is strongly frequency-dependent and varies between 1.2 dB (at about 2.5 kHz) and 13.7 dB (at about 500 Hz). This minimum around 2.5 kHz and the maximum around 500 Hz coincide with a local minimum and the maximum of the far-end signal spectrum, respectively.
5.2.3 Experimental results
In the following, two experiments will be discussed. Experiment 1 considers an adaptive identification of the nonlinear systems according to Section 5.2.1, and experiment 2 considers an offline filtering without further adaptation with previously adapted models from the end of experiment 1. While experiment 1 is affected by the initial convergence phase, experiment 2 evaluates the performance achievable in double-talk situations after convergence of the filters, which is of vital importance for a full-duplex voice communication system as well.
5.2.3.1 Experiment 1 - continuously adapted filters
5.2.3.2 Experiment 2 - echo reduction during double talk
In experiment 2 (depicted in Fig. 10 b), when processing the entire sequence offline with the converged filters from the previous experiment, the ESA-HM performs as good as the full PB-HGM in setups C and D. Only in setups A and E, the PBSA-HGM can marginally outperform the ESA-HM in experiment 2. Overall, the ESA-HM generalizes slightly better for double-talk situations than the PBSA-HGM. Interestingly, the typically considered PBSA-HGM’s HGM output and the PBSA-HGM’s HM output do not differ significantly in experiment 2, which supports the initial assumption that an HM is a suitable efficient approximation of the echo path. The increased performance due to the PBSA-HGM’s HGM submodel seems to originate from a quicker reaction to the instantaneous signal characteristics.
Clearly, the novel ESA-HM is a very well performing alternative to the PBSA-HGM, which leads to comparable or even better echo reduction in double-talk situations.
6 Conclusions
The adaptation of nonlinear echo paths for small portable devices requires efficient adaptive nonlinear echo path models. To this end, a novel variant of SA filtering has been introduced and compared to known concepts in this article. This novel ESA filtering method, leading to an adaptive ESA-HM, exploits the inability of IRs to model nonlinearities to obtain an estimate of the unobservable nonlinearly distorted loudspeaker signal by inverse filtering. While the previously proposed PBSA-HGM has been an efficient alternative to HGMs only for a block-partitioning of the filter, the novel ESA filtering concept is advantageous without block partitioning at all and for a very high number of branches (see complexity analyses in Section 5.1). For applications where very long RIRs need to be modeled and where a low input-output delay is required, a block partitioning and therefore the PBSA-HGM may computationally be more efficient than the ESA-HM. Both methods thereby complement each other very well in terms of computational efficiency for different application scenarios. A comparison of the echo reduction performance of the ESA-HM, the PBSA-HGM, a linear model, and a PB-HGM in Section 5.2 has emphasized the efficacy of the proposed ESA-HM, especially for double-talk situations, in which AEC is actually most important.
7 Endnotes
^{1}Note that this representation of a Volterra filter is also referred to as diagonal coordinate representation [66].
^{2}This update rule is also referred to as NLMS with kernel-specific normalization in [66].
^{3}As opposed to time-domain adaptive filters, the complexity for filter adaptation in the frequency domain is not determined by the length of the time-domain support of the filter, but by the DFT size N and the number of partitions P. This disqualifies unpartitioned frequency-domain adaptive filters, as the HGM submodel in Fig. 5 would have the same complexity as the HGM with full temporal support in Fig. 2 d.
^{4}Determining such a measure is also referred to as Schüßler’s model in [67], as it goes back to [68, 69].
^{5}Note that this number is caused by nonlinearity and not by background noise, as the SNR of the recorded signal is more than 24 dB in the 8−16 kHz range, where E _{out} is computed.
Declarations
Acknowledgements
The authors would like to thank the Deutsche Forschungsgemeinschaft (DFG) for supporting this work (contract number KE 890/9-1).
Competing interests
The authors declare that they have no competing interests.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
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