Patent ID: 12223977

In these figures, references identical from one figure to another designate identical or analogous elements. For reasons of clarity, the elements shown are not to scale, unless explicitly stated otherwise.

Also, the order of steps represented in these figures is provided only for illustration purposes and is not meant to limit the present disclosure which may be applied with the same steps executed in a different order.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG.1represents schematically an exemplary embodiment of an audio system10. In some cases, the audio system10is included in a device wearable by a user. In preferred embodiments, the audio system10is included in earbuds or in earphones or in smart glasses.

As illustrated byFIG.1, the audio system10comprises at least two audio sensors which are configured to measure voice acoustic signals emitted by the user of the audio system10.

One of the audio sensors is referred to as internal sensor11. The internal sensor11is referred to as “internal” because it is arranged to measure voice acoustic signals emitted by the user which propagate internally through the user's head. For instance, the internal sensor11may be an air conduction sensor (e.g. microphone) to be located in an ear canal of a user and arranged on the wearable device towards the interior of the user's head, or a bone conduction sensor (e.g. accelerometer, vibration sensor). Hence, the internal sensor11measures at least bone-conducted voice but it may also, depending e.g. on its type, measure a mix of bone-conducted and air-conducted voice. The internal sensor11may be any type of bone conduction sensor or air conduction sensor known to the skilled person.

The other audio sensor is referred to as external sensor12. The external sensor12is referred to as “external” because it is arranged to measure voice acoustic signals emitted by the user which propagate externally to the user's head (via the air between the user's mouth and the external sensor12). The external sensor12is an air conduction sensor (e.g. microphone) to be located outside the ear canals of the user, or to be located inside an ear canal of the user but arranged on the wearable device towards the exterior of the user's head. Hence, the external sensor12measures only air-conducted voice. The external sensor12may be any type of air conduction sensor known to the skilled person.

As illustrated byFIG.1, the audio system10comprises also at least one speaker unit13. In a conventional manner, each speaker unit13receives as input an audio signal, referred to as speaker audio signal, and converts into an acoustic signal (wave), referred to as speaker acoustic signal.

For instance, if the audio system10is included in a pair of earbuds (one earbud for each ear of the user), then the internal sensor11is for instance arranged with the speaker unit13in a portion of one of the earbuds that is to be inserted in the user's ear, while the external sensor12is for instance arranged in a portion of one of the earbuds that remains outside the user's ears. In some cases, the audio system10may comprise two or more internal sensors11(for instance one or two for each earbud) and/or two or more external sensors12(for instance one for each earbud) and/or two or more speaker units13(for instance one for each earbud).

As illustrated byFIG.1, the audio system10comprises also a processing circuit14connected to the internal sensor11and to the external sensor12. The processing circuit14is configured to receive and to process the audio signals produced by the internal sensor11and by the external sensor12. In the non-limitative example illustrated byFIG.1, the processing circuit14is also connected to the speaker unit(s)13. In such a case, the processing circuit14may have access to (and may even generate) the speaker audio signal(s) played by the speaker unit(s)13. It should be noted that the present disclosure can also be applied when the processing circuit14is not connected to the speaker unit(s)13and/or when said processing circuit14does not have access to the speaker audio signal(s) played by the speaker unit(s)13.

In some embodiments, the processing circuit14comprises one or more processors and one or more memories. The one or more processors may include for instance a central processing unit (CPU), a graphical processing unit (GPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), an application specific integrated circuit (ASIC), etc. The one or more memories may include any type of computer readable volatile and non-volatile memories (magnetic hard disk, solid-state disk, optical disk, electronic memory, etc.). The one or more memories may store a computer program product (software), in the form of a set of program-code instructions to be executed by the one or more processors in order to implement all or part of the steps of an audio signal processing method20.

FIG.2represents schematically the main steps of an exemplary embodiment of an audio signal processing method20for cancelling echo, which are carried out by the audio system10.

As illustrated byFIG.2, the internal sensor11measures acoustic signals reaching said internal sensor11, thereby producing an audio signal referred to as internal audio signal (step S200). Simultaneously, the external sensor12measures acoustics signals reaching said external sensor12, thereby producing an audio signal referred to as external audio signal (step S201). The acoustic signals reaching the internal sensor11and the external sensor12may or may not include a voice acoustic signal emitted by the user, with the presence of a voice activity varying over time as the user speaks.

During e.g. a voice call, the speaker unit13typically emits a speaker acoustic signal (which or may not include the far-end speaker's voice). In this case, the acoustic signals reaching the internal sensor11and the external sensor12may or may not include the speaker acoustic signal emitted by the speaker unit13, with the internal sensor11typically picking up more the speaker acoustic signal than the external sensor12, since the internal sensor11is usually much closer to the speaker unit13than the external sensor12. Also, in case of e.g. an earbud tightly fit in the ear canal of the user, the ear canal may be sealed by the earbud thereby strongly attenuating the speaker acoustic signal leaking outside the ear canal towards the external sensor12.

As illustrated byFIG.2, the audio signal processing method20comprises a step S210of converting the internal audio signal to frequency domain, executed by the processing circuit14, which produces an audio spectrum referred to as internal audio spectrum. Similarly, the audio signal processing method20comprises a step S211of converting the external audio signal to frequency domain, executed by the processing circuit14, which produces an audio spectrum referred to as external audio spectrum.

Indeed, the internal audio signal and the external audio signal are in time domain and steps S210and S211aim at performing a spectral analysis of the internal and external audio signals to obtain respective audio spectra in frequency domain. In some examples, steps S210and S211may for instance use any time to frequency conversion method, for instance a fast Fourier transform (FFT), a discrete Fourier transform (DFT), a discrete cosine transform (DCT), a wavelet transform, etc. In other examples, steps S210and S211may for instance use a bank of bandpass filters which filter the audio signals in respective frequency sub-bands of a same frequency band, etc.

For instance, the internal and external audio signals may be sampled at e.g. 16 kilohertz (kHz) and buffered into time-domain audio frames of e.g. 4 milliseconds (ms). For instance, it is possible to apply on these audio frames a 128-point DCT or FFT to produce audio spectra up to the Nyquist frequency fNyquist, i.e. half the sampling rate (i.e. 8 kHz if the sampling rate is 16 kHz).

In the sequel, we assume in a non-limitative manner that the frequency band on which are determined the internal audio spectrum and the external audio spectrum are composed of N discrete frequency values fnwith 1≤n≤N, wherein fn−1<fnfor any 2≤n≤N. For instance, f1=0 and fN=fNyquist, but the spectral analysis may also be carried out on a frequency sub-band in [0,fNyquist] For instance, f1=0 and fNis lower than or equal to 4000 Hz, or lower than or equal to 3000 Hz. It should be noted that the determination of the audio spectra may be performed with any suitable spectral resolution. Also, the frequencies fnmay be regularly spaced in some embodiments or irregularly spaced in other embodiments.

The internal audio spectrum SIof the internal audio signal sIcorresponds to a set of values {SI(fn), 1≤n≤N}. The external audio spectrum SEof the external audio signal sEcorresponds to a set of values {SE(fn), 1≤n≤N}. Typically, the internal audio spectrum SI(resp. the external audio spectrum S E) may be a complex spectrum such that SI(fn) (resp. SE(fn)) comprises both:a magnitude value representative of the power of the internal audio signal sI(resp. the external audio signal sE) at frequency fn,a phase value of the internal audio signal sI(resp. the external audio signal sE) at the frequency fn.

For instance, if the audio spectra are computed by an FFT, then SI(fn) (resp. SE(fn)) can correspond to FFT[sI](fn) (resp. FFT[sE](fn)).

It should be noted that, in some embodiments, the internal and external audio spectra can optionally be smoothed over time, for instance by using exponential averaging with a configurable time constant.

As illustrated byFIG.2, the audio signal processing method20comprises a step S220of estimating, by the processing circuit14and based at least on the internal audio spectrum, an echo audio spectrum of an echo audio signal caused by the speaker unit13in the external audio signal. Indeed, as discussed above, both the external sensor12and the internal sensor11may pick-up the speaker acoustic signal emitted by the speaker unit13, thereby causing the presence of echo in the external audio signal and in the internal audio signal. The internal sensor11typically picks up more the speaker acoustic signal than the external sensor12, and it is proposed to use the internal audio spectrum to estimate the echo audio spectrum in the external audio spectrum, i.e. to estimate the audio spectrum of the part of the external audio signal which represents the measurement of the speaker acoustic signal by the external sensor12.

As will be discussed hereinbelow, depending on the embodiments, the echo audio spectrum may be a complex spectrum as discussed above (with a magnitude value and a phase value for each considered frequency), or a magnitude spectrum such that it comprises only a magnitude value representative of the estimated power, for each considered frequency, of the echo audio signal included in the external audio signal.

As illustrated byFIG.2, the audio signal processing method20comprises a step S230of correcting the external audio spectrum, by the processing circuit14and based on the estimated echo audio spectrum.

As discussed above, the internal audio signal produced by the internal sensor11contains:ambient noise mainly in low frequencies, i.e. up to 1-2 kHz for an air conduction sensor and up to 600 Hz-1 kHz for a bone conduction sensor,bone-conducted voice mainly in low frequencies, i.e. up to 1-2 kHz for an air conduction sensor and up to 600 Hz-1 kHz for a bone conduction sensor,echo in all the spectral bandwidth of the internal sensor11.

Hence, in high frequencies, the internal sensor11picks-up mostly echo. In low frequencies, the internal audio signal also contains audio signals from other sources such that it may be more difficult to estimate accurately the echo in low frequencies by using the internal audio spectrum. Even if the echo audio spectrum may be estimated for both high frequencies and low frequencies, it is preferable to correct the external audio spectrum, based on the estimated echo audio spectrum, only in high frequencies to prevent from injecting low frequency undesirable audio signals (additional noise and/or bone-conducted voice) into the external audio spectrum.

Accordingly, in preferred embodiments, the correction (echo cancellation) of the external audio spectrum based on the estimated echo audio spectrum is carried out only for frequencies above a predetermined minimum frequency fmin(f1<fmin<fN) Basically, the minimum frequency fminis a frequency above which the ambient noise and the user's voice may be considered negligible with respect to echo in the internal audio signal. Hence, the echo audio spectrum is estimated at least for frequencies above the minimum frequency fmin(and it can be estimated only for frequencies above the minimum frequency fmin, i.e. it needs not to be estimated for frequencies below the minimum frequency fminif the correction is carried only for frequencies above the minimum frequency fmin).

The minimum frequency fminis a predetermined frequency that may depend on the type of the internal sensor11(i.e. air conduction sensor or bone conduction sensor). The minimum frequency fminmay be greater than or equal to 600 Hz, or preferably greater than or equal to 1 kHz, in particular if the internal sensor11is an air conduction sensor. The minimum frequency fminis preferably lower than or equal to 2.5 kHz. For instance, the minimum frequency fminis a predetermined frequency in the frequency range [1 kHz, 2 kHz]. In some embodiments, the minimum frequency fminmay be predefined beforehand and remain static (unchanged) over time. In other embodiments, the minimum frequency fminmay vary over time according to e.g. operating conditions of the audio system10. For instance, the minimum frequency fminmay vary in the frequency range [1 kHz, 2 kHz] depending on the user's voice activity. For, instance, if it is determined that the user's voice is not present in the audio signals, the minimum frequency fminmay be decreased, for instance set to 1 kHz, in order to try and increase the frequency range over which the external audio spectrum is corrected. In turn, if it is determined that the user's voice is present in the audio signals, the minimum frequency fminmay be increased, for instance set to 2 kHz, in order to reduce the frequency range over which the external audio spectrum is corrected.

We now present non-limitative exemplary embodiments for steps S220and S230. We assume in a non-limitative manner that the echo audio spectrum is estimated only above the minimum frequency fminin order to correct the external audio spectrum only above said minimum frequency fmin. As discussed previously, the internal audio spectrum corresponds mainly to echo above the minimum frequency fmin. Above the minimum frequency fmin(i.e. for fminfnfN), the internal audio spectrum and the external audio spectrum may be expressed as:
SI(fn)=EI(fn)
SE(fn)=EE(fn)+XE(fn)
wherein:EIcorresponds to the echo audio spectrum of the echo audio signal in the internal audio signal, referred to as internal echo audio spectrum,EEcorresponds to the echo audio spectrum of the echo audio signal in the external audio signal, referred to as external echo audio spectrum, that we want to estimate during step S220,XEcorresponds to the audio spectrum of other audio signals present in the external audio signal (ambient noise, voice, etc.).

The external echo audio spectrum/EEmay be expressed as:
EE(fn)=GE(fn)EI(fn)

Indeed, as discussed above, the internal audio signal in high frequencies includes the non-linear components that cannot be modeled by prior art solutions. Hence, the non-linear components are present in the internal echo audio spectrum EIsuch that the difference between EI(fn) and EE(fn) may be modeled by a complex weight GE(fn).

Accordingly, in order to estimate the external echo audio spectrum EE, it is possible to estimate each complex weight GE(fn) (or only its magnitude, depending on the embodiments). The estimated external echo audio spectrum, designated by EE, can then be computed as:
ÊE(fn)=ĜE(fn)SI(fn)
wherein ĜE(fn) corresponds to the estimate of the complex weight GE(fn). As discussed above, the estimated external echo audio spectrum may be a complex spectrum EEor a magnitude spectrum ∥ÊE∥. For instance, the magnitude value (i.e. modulus or absolute level of EE(fn)), or to ∥ÊE(fn)∥ corresponds to |ÊE(fn)| (i.e. modulus or absolute level of ÊE(fn)), or to |EE(fn)|2(i.e. power of EE(fn)). If the estimated external echo audio spectrum corresponds to a magnitude spectrum ∥ÊE∥, it can be computed based on each estimated magnitude weight ∥ĜE(fn)∥ as follows:
∥ÊE(fn)∥=∥ĜE(fn)∥∥SI(fn)∥

For instance, the choice of estimating a complex spectrum ÊEor a magnitude spectrum ∥ÊE∥ depends on how the correction is performed in step S230. If the goal of the correction is to actually remove (subtract) the estimated echo audio spectrum from the external audio spectrum SE, then a complex spectrum ÊEmay be estimated, by determining estimated complex weights ĜE(fn). If the goal is to attenuate frequency components of the external audio spectrum SEwhich include echo, then estimating a magnitude spectrum ∥ÊE∥ is sufficient, by determining estimated magnitude weights ∥ĜE(fn)∥.

Generally, the determination of the estimates ĜE(fn) or ∥ĜE(fn)∥ (which yield the estimates ÊEor ∥ÊE∥) may use any estimation method known to the skilled person, and the choice of a specific estimation method corresponds to a specific and non-limitative embodiment of the present disclosure.

For instance, the complex weights GE(fn) may be estimated by searching for the estimated complex weight ĜE(fn) which minimizes a distance with the external audio spectrum SE, for instance according to the following expression:
E[|SE(fn)−ĜE(fn)SI(fn)|2]

This corresponds to a classic least mean square problem for linear model identification that can be solved for instance by using a frequency domain normalized least mean square (NLMS) filter, or any frequency domain adaptive filter known to the skilled person.

As discussed above, the estimated complex weights ĜE(fn) directly yield the estimated external echo audio (complex) spectrum ÊEas follows:
ÊE(fn)=ĜE(fn)SI(fn)

The correction of the external audio spectrum, during step S230, may for instance correspond to an echo cancellation, in which case it may be carried out by subtracting (on a frequency by frequency basis) the estimated external echo audio (complex) spectrum from the external audio spectrum. The corrected external audio spectrum, designated by SE, may be expressed as follows (with fmin≤fn≤fN):
ŜE(fn)=SE(fn)−ĜE(fn)SI(fn)

It should be noted that other expressions may also be used when the correction corresponds to an echo cancellation. For instance, it is possible to compute a weighted subtraction seeking to reduce the echo in the external audio spectrum without necessarily seeking to completely remove it from the external audio spectrum.

As discussed above, in some cases, it can be sufficient to estimate the magnitude weights ∥GE(fn)∥ and the external echo audio (magnitude) spectrum ∥EE∥. For instance, the magnitude weights ∥GE(fn)∥ may be estimated recursively, and for each iteration k:
∥ĜE(fn)∥k=α∥ĜE(fn)∥k−1+(1−α)(∥SE(fn)∥)
wherein 0<α<1 is a smoothing constant. In this example, a new iteration is executed each time the estimated magnitude weights ∥ĜE(fn)∥ need to be updated, for instance for each audio frame or group of audio frames, or each time a predetermined updating criterion is satisfied (for instance linked to whether or not an echo is present), etc.

As discussed above, the estimated magnitude weights ∥ĜE(fn)∥ directly yield the estimated external echo audio (magnitude) spectrum ∥ÊE∥ as follows:
∥ÊE(fn)∥=∥ĜE(fn)∥∥SI(fn)∥

In such a case, the correction of the external audio spectrum, during step S230, may correspond to an echo suppression, in which case it may seek to attenuate frequency components of the external audio spectrum SEwhich are impacted by echo. In such a case, the estimated external echo audio (magnitude) spectrum ∥ÊE∥ may be used, during step S230, to compute echo suppression gains that will subsequently be applied to the external audio spectrum. According to a non-limitative example, computing echo suppression gains first comprises estimating, for each frequency fn(with fmin≤fn≤fN), a signal to echo ratio. For instance, the signal to echo ratio estimates(fn) (with fmin≤fn≤fN) may be determined as follows:

(fn)=SE(fn)GˆE(fn)⁢SI(fn)

The signal to echo ratio estimates(fn) may then be used to determine the echo suppression gains GM(fn) for attenuating the magnitude of the frequency components of the external audio spectrum which are impacted by echo. For instance, the echo suppression gains GM(fn) can be computed by using a predetermined gain mapping function gM:
GM(fn)=gM((fn))

FIG.3represents schematically two non-limitative examples for the gain mapping function, designated respectively gM,1and gM,2. InFIG.3, the echo suppression gains are in decibels (dB) and, since they aim mainly at attenuating frequency components of the external audio spectrum SEwhich are impacted by echo, the echo suppression gains are lower than or equal to 0 dB.

The echo suppression gains GM(fn) are then applied by multiplying (on a frequency by frequency basis) the computed echo suppression gains to the estimated external echo audio (complex) spectrum to the external audio (complex) spectrum. The corrected external audio spectrum, designated by SE, may be expressed as follows (with fmin≤fn≤fN):
ŜE(fn)=GM(fn)SE(fn)

The echo suppression gains GM(fn) are real numbers (vs. complex numbers) which modify only the magnitudes of the external audio spectrum.

Several non-limitative examples have been provided hereinabove for steps S220and S230. It should be noted that other examples are possible for estimating the echo audio spectrum of the echo audio signal in the external audio signal (step S220) and for correcting the external audio spectrum (step S230).

As discussed above, the proposed solution, as illustrated byFIG.2, can be used as a standalone echo mitigation (cancellation or suppression) solution which uses the internal audio signal as echo reference, or in combination with conventional echo cancellation algorithms which use the speaker audio signal as echo reference. For instance, such conventional echo cancellation algorithms can be used for cancelling echo:in the external audio signal only, orin the internal audio signal only, orin both the external audio signal and the internal audio signal.

Since the proposed solution aims at mitigating echo in the external audio signal by using the internal audio signal, echo cancellation based on the speaker audio signal, when present, is preferably performed at least on the external audio signal, and preferably before applying the proposed solution.

FIG.4represents schematically the main steps of an exemplary embodiment of the audio signal processing method20, in which echo cancellation based on the speaker audio signal (i.e. the audio signal provided as input to the speaker unit13) is carried out on both the external audio signal and the internal audio signal. In addition to the steps discussed in relation toFIG.2, the audio signal processing method20inFIG.4comprises:a step S260of performing echo cancellation on the internal audio signal based on the speaker audio signal,a step S261of performing echo cancellation on the external audio signal based on the speaker audio signal.

In the example illustrated byFIG.4, the echo cancellation on the external audio signal based on the speaker audio signal (step S261) is carried out before correcting the external audio spectrum (step S230). In the example illustrated byFIG.4, the echo cancellation on the external audio signal based on the speaker audio signal (step S261) is further carried out in time domain and is therefore performed before converting the external audio signal to frequency domain (step S211).

In the example illustrated byFIG.4, the echo cancellation on the internal audio signal based on the speaker audio signal (step S260) is carried out before estimating the external echo audio spectrum based on the internal audio spectrum (step S220). In the example illustrated byFIG.4, the echo cancellation on the internal audio signal based on the speaker audio signal (step S260) is further carried out in time domain and is therefore performed before converting the internal audio signal to frequency domain (step S210).

Generally speaking, each of steps S260and S261may use any echo cancellation method based on the speaker audio signal known to the skilled person, and the choice of a specific echo cancellation method corresponds to a specific and non-limitative embodiment of the present disclosure.

In some examples, each of step S260and step S261may for instance adaptive filtering techniques, for instance based on a least mean square (LMS) filter or based on a NLMS filter. For instance, performing echo cancellation on the external audio signal (resp. internal audio signal) based on the speaker audio signal comprises filtering the speaker audio signal by a first adaptive filter (resp. second adaptive filter), for instance using the external audio signal (resp. internal audio signal) as reference for computing the error, and correcting the external audio signal (resp. internal audio signal) based on the filtered speaker audio signal.

FIG.5represents schematically the main steps of an exemplary embodiment of the audio signal processing method20. In addition to the steps discussed in relation toFIG.2, the audio signal processing method20inFIG.5comprises an optional step S270of determining whether or not an echo audio signal is present. Indeed, if there is no echo audio signal in the current audio frame, then it is not necessary to correct the external audio spectrum (step S230). In other words, correcting the external audio spectrum (step S230) is performed only when it is determined during step S270that an echo audio signal is present in the current audio frame. Also, if the correction of step S230is not performed, then other steps need not to be executed. For instance, if the correction (step S230) is not performed for the current audio frame, then it is not necessary to estimate the external echo audio spectrum (step S220) (for instance, it is not necessary to perform an iteration when the magnitude weights ∥GE(fn)∥ are estimated recursively, etc.).

Determining whether or not an echo audio signal is present may use any echo detection method known to the skilled person, and the choice of a specific echo detection method corresponds to a specific and non-limitative embodiment of the present disclosure. For instance, it is possible the determine a level of the speaker audio signal. If the level of the speaker audio signal is low, then it is likely that no echo audio signal is present in the external audio signal. In turn, if the level of the speaker audio signal is high, then it can be considered that an echo audio signal is present in the external audio signal.

In the example illustrated byFIG.5, the audio signal processing method20comprises also the optional echo cancellation steps S260and S261which use the speaker audio signal. In such a case, it is possible to determine whether or not an echo audio signal is present in the current audio frame by e.g. comparing an output power of the internal audio signal (resp. external audio signal) after the echo cancellation step S260(resp. step S261) with an input power of the internal audio signal (resp. external audio signal) before the echo cancellation step S260(resp. step S261). For instance, if a ratio between the output power and the input power is low (close to 0), then this indicates that the internal audio signal (resp. external audio signal) is mostly echo and it can be considered that an echo audio signal is present in the external audio signal. In turn, if the ratio between the output power and the input power is high (close to 1), then this indicates that the internal audio signal (resp. external audio signal) is mostly not echo and it can be considered that no echo audio signal is present in the external audio signal.

Hence, the proposed audio signal processing method20enhances the mitigation, in the external audio signal, of components of the echo which are non-linear with respect to the speaker audio signal fed to the speaker unit13.

In some embodiments, it is also possible to combine the corrected external audio signal (obtained after step S230) with the internal audio signal for e.g. mitigating noise. In such a case, and as illustrated byFIGS.2,4and5, the audio signal processing method20further comprises an optional step S240of producing an output signal by combining the corrected external audio signal with the internal audio signal. For instance, the output signal is obtained by using the internal audio signal below a cutoff frequency and using the corrected external audio signal above the cutoff frequency. Typically, the output signal may be obtained by:low-pass filtering the internal audio signal based on the cutoff frequency,high-pass filtering the corrected external audio signal based on the cutoff frequency,adding the respective results of the low-pass filtering of the internal audio signal and of the high-pass filtering of the corrected external audio signal to produce the output signal.

For instance, the cutoff frequency may be a static frequency, which is preferably selected beforehand. Preferably, the cutoff frequency is lower than or equal to the minimum frequency fmin, since the corrected external audio signal can be supposed to comprise less echo above the minimum frequency fminthan the internal audio signal.

According to another example, the cutoff frequency may be dynamically adapted to the actual noise conditions. For instance, the setting of the cutoff frequency may use the method described in U.S. patent application Ser. No. 17/667,041, filed on Feb. 8, 2022, the contents of which are hereby incorporated by reference in its entirety.

It should be noted that the combining of the corrected external audio signal with the internal audio signal may be performed in time domain or in frequency domain. Since the echo mitigation using the internal audio signal as echo reference uses frequency domain signals, it is advantageous to combine the corrected external audio signal with the internal audio signal in frequency domain, as illustrated byFIGS.2,4and5. In such a case, the internal audio signal and the corrected external audio signal are combined by combining the internal audio spectrum and the corrected external audio spectrum, thereby producing an audio spectrum of the output signal, referred to as output audio spectrum. For instance, if the cutoff frequency is lower than or equal to the minimum frequency fmin, the output audio spectrum can be substantially identical to the corrected external audio spectrum for at least frequencies above said minimum frequency fmin.

When the combining is performed in frequency domain, and as illustrated byFIGS.2,4and5, the audio signal processing method20can for instance further comprise a step S250of converting to time domain the output spectrum, to produce the output signal in time domain.

When the combining is performed in time domain, then the audio signal processing method20can for instance comprise, before the combining step S240, a step, not represented in the figures, of converting to time domain the corrected external audio spectrum which produces the corrected external audio signal which can be combined with the internal audio signal in time domain.

It is emphasized that the present disclosure is not limited to the above exemplary embodiments. Variants of the above exemplary embodiments are also within the scope of the present invention.

For instance, the present disclosure has been provided by considering mainly an echo mitigation solution, using the internal audio signal as echo reference, which is carried out in frequency domain. Indeed, implementing the proposed echo mitigation solution in frequency domain is particularly advantageous, in particular because it facilitates the mitigation of echo in a frequency selective manner above the minimum frequency fmin. However, in other embodiments, the proposed echo mitigation solution, which uses the internal audio signal as echo reference, can also be carried out in time domain.