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Acoustic Echo Cancellation for Low Cost Applications Alango approach Interactive white paper by Alexander A. Goldin.

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Presentation on theme: "Acoustic Echo Cancellation for Low Cost Applications Alango approach Interactive white paper by Alexander A. Goldin."— Presentation transcript:

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2 Acoustic Echo Cancellation for Low Cost Applications Alango approach Interactive white paper by Alexander A. Goldin

3 2 Presentation roadmap Acoustic echo cancellation in mobile voice communication Place of acoustic echo cancellation in voice communication Textbook acoustic echo cancellation The real acoustic echo cancellation problem The real acoustic echo cancellation problem Requirements for a practical Acoustic Echo Canceller Convergence of Alango adaptive filter in double talk Advantages of subband adaptive filtering: DSP clock Advantages of subband adaptive filtering: Convergence Logic of Alango residual echo suppressor Advantages of subband residual echo suppression Alango Acoustic Echo Canceller (all parts together) Alango Acoustic Echo Canceller

4 3 Acoustic echo cancellation in mobile voice communication Acoustic echo arises due to coupling between the speaker and the microphone of a communication device. Part of the signal from the far side reproduced by the device speaker is picked by the microphone and transmitted back to the far side by a communication link which may be wired or wireless, analog or digital. The far talker perceives this as echo which is in some cases simply annoying while in others completely prevents efficient voice communication. Acoustic Echo Cancellation (AEC) should remove all noticeable echo of the far speech from the microphone signal while preserving the near speech quality. If it does a great job, the communication is called true full-duplex where both near and far side may talk and hear simultaneously. In some circumstances it can really be achieved, in others we can get close to it while in some extreme cases efficient echo cancellation is not possible and we have effectively resort to half-duplex communication. Ability to provide duplex communication is defined by AEC technology at hand, type of application, used acoustic components and acoustic design. Examples are provided by two pictures below. For a mobile phone working in the speakerphone mode the speaker volume and the corresponding acoustic echo is much larger while the near talker voice is relatively weak. Often overdriven speaker has large distortions and possible mechanical resonances inside the phone cavity complicate the case. The task of acoustic echo cancellation becomes extremely challenging and the true full duplex communication is very difficult to achieve. For a mobile phone working in the handset mode the speaker volume is relatively low so that the initial echo is rather small. With proper acoustic components and device acoustic design, the speaker distortions and the mechanical coupling between the speaker and the microphone are also small. As a result, the task of acoustic echo cancellation is relatively simple so that true full duplex communication rather easy to achieve. Large speech/echo ratio Small speaker distortions Small speech/echo ratio Large speaker distortions

5 4 Place of acoustic echo cancellation in voice communication Acoustic Echo Canceller is responsible for cleaning voice communication from acoustic echo. To be able to differentiate between echo and near side talk, Acoustic Echo Canceller is provided with the reference, speaker signal. Assuming that the far and near talks are not correlated (as signals), Acoustic Echo Canceller compares the speaker (reference) and the microphone (primary) signals trying to remove all parts of the microphone signal that are correlated with the reference signal. The term correlation is used here in wide, human sense rather than in according to strict mathematical definition. To improve correlation in both human and mathematical sense, Acoustic Echo Canceller must be the first signal processing block that gets the microphone signal. No non-linear processing such as automatic gain control or signal distortions such as signal clipping is allowed on the microphone signal. Accordingly, the reference signal must be the speaker signal just before digital-to-analog conversion. No other processing or clipping on the power amplifier should be performed on the analog signal. The speaker volume control must be implemented digitally before the speaker signal is taken for reference to Acoustic Echo Canceller.

6 5 Textbook acoustic echo cancellation Algorithm goal: reduce the error (echo) signal e(n) as much and fast as possible Least Mean Squares (LMS) Least Mean Squares (LMS) Normalized Least Mean Squares (NLMS) Normalized Least Mean Squares (NLMS) Variable step NLMS Variable step NLMS Recursive Least Squares (RLS) Recursive Least Squares (RLS) Frequency domain LMS Frequency domain LMS Affine Projection Algorithms (APA) Affine Projection Algorithms (APA) Others … Others … Do we need anything else ? Factors to consider: convergence speed, missadjustment (error), complexity, memory Variety of adaptive filtering algorithms is available: Textbook algorithms address a simplified problem where: - The primary (microphone) signal contains acoustic echo only. - Echo is considered to be a convolution of the reference (speaker) signal r(t) with the echo path F(t). Standard textbook approaches simulates the echo path by FIR filter with variable, adaptive coefficients. The filtered reference signal that is an estimation of the echo signal is subtracted from the primary signal.

7 6 The real acoustic echo cancellation problem Textbook, adaptive filtering approach addresses an unrealistic problem: - Due to system non-linearities, only part of the echo signal is described by the convolution.The other part is non-linear. It is still perceived as echo (correlated to the reference signal in wide, human sense). - Besides the echo, primary signal contains additional components such as near speech and noise. 5-10% loudspeaker distortion is normal for mobile devices. Simulating non-linear echo by linear filtering is not possible. Strong near talk signal in double talk situation (when both sides are active) may lead to adaptive filter divergence. Near side talk, noise and non-linear echo represent wrong error signal for the adaptive filter causing wrong adaptation Strong near side noise (stationary and non-stationary) complicates using double-talk detector to disable adaptation in double talk (always ON where the far side is active).

8 7 Requirements for a practical Acoustic Echo Canceller To filter all echo and noise components while preserving the near talk, a practical Acoustic Echo Canceller requires - Adaptive filtering algorithm that: Does not diverge from (or better converge to) the true echo simulation in double talk. Converges to the true echo in high near side noise. Does not diverge from (or better converge to) the true echo in silence. - Adaptive filtering algorithm that: Does not diverge from (or better converge to) the true echo simulation in double talk. Converges to the true echo in high near side noise. Does not diverge from (or better converge to) the true echo in silence. - Residual echo suppressor to block non-linear and residual linear echoes; - Residual echo suppressor to block non-linear and residual linear echoes; - Noise Suppressor for attenuating ambient near side noise. - Noise Suppressor for attenuating ambient near side noise. Besides performance requirements, a practical Acoustic Echo Canceller should: - Take as small computational resources as possible (DSP clock < 20MIPS with dual MAC DSP, RAM < 8KW). - Take as small computational resources as possible (DSP clock < 20MIPS with dual MAC DSP, RAM < 8KW). - Do not Introduce a large processing delay (<50ms). - Do not Introduce a large processing delay (<50ms).

9 8 Alango Acoustic Echo Canceller Alango Acoustic Echo Canceling technology answers all the requirements for a practical echo canceller. It implements a proprietary adaptive filtering algorithm converging in double talk. It includes a residual echo suppressor that automatically tracks performance of echo canceller and suppress the echo if it is not masked by the near side voice. It also seamlessly integrates a high performance noise suppressor. For the highest performance it utilizes subband processing where the input (primary and reference) signals are first divided on multiple frequency subbands. Each band is processed independently and the outputs are combined into a full band output signal. Subband processing provides multiple advantages over one (full) band processing. These advantages are explained and demonstrated in the following slides. Near side Far side Alango acoustic echo canceling technology is fully scalable such that the number of subbands may be chosen to provide the best tradeoff between performance, MIPS, memory and processing delay requirements. Standard options include 8, 16, 32 subbands

10 9 Convergence of Alango adaptive filter in double talk Textbook (NLMS) adaptive filter Far talk Near side Far side Acoustic Echo Cancellation in continuous double talk posts significant challenge. Adaptive filtering algorithms try to remove all components of the primary signal that are correlated with the reference signal. Far and near talk signals are not (mathematically) correlated in a long run. However, the real life speech signals will always be correlated to some extent when correlation is computed in a short time interval. In general, the shorter time interval, the larger random correlations between the two speech signals are. For fast initial convergence and good tracking of possible changes in the echo path, the filter must have fast adaptation rate. However, during double talk a fast adaptation rate leads to wrong adaptation as the filter tries to adapt to random, short-time correlations between the near and far talk signals. If the problem is not addressed specifically, a good tradeoff between the convergence speed and double talk performance is not possible to achieve. If talk detector is used in an Echo Canceller, it is supposed to resolve the problem of wrong adaptation by disabling or slowing the filter adaptation when double talk is detected. Creating a reliable double talk detector is a challenge by itself. However, even a perfect detector does not provide the ultimate solution as changes in the echo path cannot be followed in double talk. Besides, with high level of near side noise (especially non- stationary), there will be continues double talk situation so that no filter adaptation will occur. If double talk detector is used in an Echo Canceller, it is supposed to resolve the problem of wrong adaptation by disabling or slowing the filter adaptation when double talk is detected. Creating a reliable double talk detector is a challenge by itself. However, even a perfect detector does not provide the ultimate solution as changes in the echo path cannot be followed in double talk. Besides, with high level of near side noise (especially non- stationary), there will be continues double talk situation so that no filter adaptation will occur. Alango proprietary adaptive filtering algorithm implements a control logic that provides robustness and convergence in double talk without explicitly slowing down the filter adaptation rate. The audio example below demonstrate the algorithm performance in continuous double talk situation. Press the corresponding button to here the reference (far side) signal, the primary (microphone) signal before processing, the processing result by Alango full- band adaptive filter as well as a textbook NLMS algorithm for comparison. Advantages of subband adaptive filtering and suppression of residual echo left after adaptive filtering are explained on next slides. Acoustic echo No processing ( mic. signal ) Alango adaptive filter Near speech Noise

11 10 Advantages of subband adaptive filtering: DSP clock Full band adaptive filter T=0.1s – filter time span L F = F S x T Sample rate F S =8000 Full band complexity: F S x L x R LMS = 8000x800x5 = 32 MIPS Subband complexity: N x ( Fs/M x L x C LMS ) = 32x(250x25x20) = 4 MIPS or 8 times reduction!!! Complexity of LMS type of algorithms is proportional to the filter length. Using R LMS = 5 as realistic LMS factor (instructions per filter coefficient per input sample), we come to the following estimation Subband adaptive filtering scheme provides significant saving of DSP clock compared to the full band implementation where the adaptive filter covers the same time span T. As an example, well consider a real life voice communication scenario where the adaptive filter length corresponds to T=100ms. For the standard sampling frequency F S =8KHz, the corresponding full band filter length L F will be 800 taps. Let us consider the case when the input signals are divided on N complex frequency subbands, adaptive filtering is performed independently in each subband (Alango technology uses complex subband filters). For illustration, the subband decomposition stage is represented as Band Pass Filtering (BPF) followed by downsampling by factor M. To cover the same time span on the downsampled signals, the subband filters will have to be M times shorter. There are N such filters but the filters operate on complex signals with the sampling rate reduced by M. BPF N M M BPF 1 M M BPF N BPF 1 Subband adaptive filter L S = F S x T / M Subband adaptive filter F S =8000/M In Alango technology M=N. For M=32 the sampling rate in each subband is reduced to F S =8000/32=250Hz and the filters are only L=800/32=25 taps length. Complex operations are more consuming (4 real multiplications to implement one complex). Thus well use a complex LMS factor as: C LMS = R LMS x4 = 20. Putting all together, we have:

12 11 Advantages of subband adaptive filtering: Convergence Full band spectral range Subband spectral range To compare performance of Alango full band and subband adaptive filtering technologies on the same double talk signals, use the action buttons Fig.1 Typical speech spectrum Fig.2 Typical speech spectrum divided on subbands Microphone Full band adaptive filter Subband adaptive filter Alango full band adaptive filter Alango subband adaptive filters Algorithms of LMS type are the most widely used due to their low complexity, low memory requirements and efficiency of DSP implementation. However, convergence of LMS types of adaptive filtering algorithms is inverse proportional to the spectral diversity of its input signals (ratio of the strongest and weakest spectral components). A typical speech signal spectrum is shown on Fig.1 below and it is seen to have a relatively large spectral diversity with most energy concentrated in low frequency region ( Hz). As such, full band LMS algorithms perform worse on speech signals than on a white noise. Subband processing divides the whole spectrum on narrow frequency subbands so that the spectral diversity in each band is much smaller (see Fig.2). As a result subband adaptive filters converge faster and better than an equivalent full band filter. As it is heard from the examples, in real conditions not all acoustic echo can be removed by adaptive filtering alone. The residual echo must be suppressed by other means. The logic and implementation of Alango residual echo suppressor is discussed on next slides

13 12 Logic of Alango residual echo suppressor Alango residual echo suppression works in the same frequency subbands as the adaptive filters and it is based on the following general principal: attenuate a frequency band if the residual echo in it is not masked by near talk. Make decision: If Signal to echo ratio T(n)/E R (n) is larger than a threshold Then pass the signal Else substitute comfort noise of amplitude N(n) Estimate: Speaker (reference) signal amplitude: R(n)Speaker (reference) signal amplitude: R(n) Microphone (primary) signal amplitude: P(n)Microphone (primary) signal amplitude: P(n) Echo Return Loss (ERL): V(n)Echo Return Loss (ERL): V(n) Echo Return Loss Enhancement (ERLE): Q(n)Echo Return Loss Enhancement (ERLE): Q(n) Initial Echo amplitude: E I (n)= R(n) x V(n)Initial Echo amplitude: E I (n)= R(n) x V(n) Residual Echo amplitude: E R (n)= E(n) x Q(n)Residual Echo amplitude: E R (n)= E(n) x Q(n) Near Talk amplitude: T(n)= P(n) - E I (n)Near Talk amplitude: T(n)= P(n) - E I (n) Near talk signal to residual echo ratio: T(n)/E R (n)Near talk signal to residual echo ratio: T(n)/E R (n) Estimate noise amplitude N(n)Estimate noise amplitude N(n) In real life situations Acoustic echo cannot be sufficiently eliminated by an adaptive filter alone. The adaptive filter must be followed by Residual Echo Suppressor must attenuating the residual echo to a level where it is unnoticeable to the person on the far side. Residual Echo Suppressor is inherently a nonlinear processor as most of the remaining echo arises due to system (mainly speaker) nonlinearities and it is not linearly related to the reference signal. As such, Residual Echo Suppressor logic cannot be described mathematically making it the most challenging block of a practical Acoustic Echo Canceller. For the place of Residual Echo Suppressor in Acoustic Echo Canceller, see Slides 8,14. The structure of Alango Residual Echo Suppressor subband block is shown on the left and its logic is explained below. Remember that all signals are subband signals.

14 13 Advantages of subband residual echo suppression Comfort noise only Original microphone signal Residual Echo Near speech Comfort noise Original microphone signal Residual Echo Near speech Microphone signal amplitude Spectrum after adaptive filter Spectrum after full band residual echo suppressor Spectrum after subband residual echo suppressor After adaptive filter Full band echo suppressor with comfort noise Subband echo suppressor without comfort noise Subband echo suppressor with comfort noise In the full band implementation the microphone may be either closed or open depending on either the full residual echo is masked by a near side speech or not. If the spectrum of the residual echo and the near side speech do not overlap, no masking occurs and the microphone channel will be closed each time there is an activity on the speaker channel. This essentially leads to half-duplex performance. The situation after the echo canceller and the corresponding full band decision is depicted on the upper and middle figures on the left. In the subband implementation (see the bottom figure), the decision are taken independently for each band. This allows passing part of the near side speech even when there is a strong residual echo in some frequency region. In general, this is the region where the energy of the near speech is small so that associated distortions are not actually noticeable. Whenever the whole microphone channel or some of its frequency bands are closed, comfort noise should be substituted instead. Without it even the best residual echo suppressor will sound choppy. The be unnoticeable, spectral properties of comfort noise mast match those of the real ambient one. Subband implementation makes it easier. Press corresponding buttons to compare full band and subband implantations of residual echo suppressor working on the same adaptive filter output Performing Residual Echo Suppression in narrow frequency bands provides significant advantages over the full band implementation. From adaptive filter Full band residual echo suppressor Subband residual echo suppressor Comfort noise Control Comfort noise Control Comfort noise Control Band 1 Band N

15 14 Alango Acoustic Echo Canceller (all parts together) Speaker Near side Far side No processing (mic. Signal) Subband adaptive filter Comfort noise Subband echo suppressor Noise Suppression Acoustic echo Near voice Noise On this slide we can see and hear all components of Alango Acoustic Echo canceller working together in a continuous double talk situation with some noise at the near side. Press the corresponding buttons to listen for processing results after each stage.

16 15 Alango Acoustic Echo Canceller (all parts together) To some extent, it is possible to evaluate Acoustic Echo Cancellation technology off-line, using signals prerecorded in specific conditions. However, the final test should always be done in real time where two persons speak from near and far sides. Human sound perception depends in a large extent on weather the person. As such, only in real time talk one is able to understand the real quality in double talk. Real time evaluation requires establishing a voice communication link. However, when using a voice communication network, the processing being done by Acoustic Echo Canceller is integrated with network processing (e.g. line echo canceling), delays, packet losses and other problems. As such, the results depend on the current network conditions To remove these addition processing components, Alango developed a special evaluation kit where the communication line is simulated by a long cable. The structure of the evaluation kit and its photograph are shown on the pictures below. The kit consists of the DSP box where the technology is implemented. It may be one of DSP evaluation boards that are supported by Alango technology. The DSP box is connected to an interface box with four amplifiers (to mic amplifiers and two power amplifiers). On one side the interface box is connected to acoustic components of the voice terminal where the technology is supposed to cancel acoustic echoes. On the other side it is connected to a standard telephone handset via 10m cable. The cable is long enough to go to another room to minimize acoustic coupling between far and near sides. The kit is really plug and talk so that no dialing is necessary. It is also easy to try the technology with different acoustic components packed into different enclosures.

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