Patent Description:
Detection of speech in incoming sound, such as microphone signal(s) generated in response to the incoming sound, of head-wearable communication devices like hearing aids, hearing instruments, active noise suppressors, headsets etc. is important for numerous signal processing purposes. Speech is often the target signal of choice for optimization of various processing algorithms and functions of the device such as environmental classifiers and noise reduction. For example aggressive speech enhancement, or noise reduction, is only desired at very low and negative SNRs. Example speech detectors are provided in e.g. the patent document <CIT>, or in the patent application <CIT>.

These signal processing algorithms often provide best performance at positive signal-to-noise ratios (SNRs) of the incoming sound at the microphone arrangement. Unfortunately, SNRs in challenging sound environments are often lower and negative and the user or patient of the head-wearable communication device may regularly be subjected to such challenging sound environments. Therefore, there is a need for reliably detecting the presence of speech, and possibly estimating speech power, to the head-wearable communication device. The reliable detection of speech at low and negative SNRs of the incoming sound allows the head-wearable communication device to appropriately steer various signal processing algorithms and avoid, or at least reduce, unwanted distortion of an incoming or received speech signal of the incoming sound. For example, when applying noise reduction algorithms to the incoming sound signal it is important to avoid distorting the target speech in the process to maintain speech intelligibility and patient or user comfort.

A first aspect of the invention relates to a method of detecting speech of incoming sound at a portable communication device as set forth in independent claim <NUM> and a corresponding speech detector configured to carry out or implement the methodology, as set forth in independent claim <NUM>. The method comprises:.

The frequency division or split of the microphone signal into the plurality of separate frequency band signals may be carried out by different types of frequency selective analog or digital filters for example organized as a filter bank operating in either frequency domain time domain as discussed in additional detail below with reference to the appended drawings. The first frequency band signal may comprises frequencies of the incoming sound between <NUM> and <NUM>, such as between <NUM> and <NUM>, for example obtained by filtering the incoming sound signal by a first, or low-band, filter configured with appropriate cut-off frequencies, e.g. a lower cut-off frequency of <NUM> and upper cut-off frequency of <NUM>. Hence, the first, or low-band, filter preferably possesses a bandpass frequency response which suppresses subsonic frequencies of the incoming sound, e.g. because these merely comprises low-frequency noise components, and suppresses very high frequency components.

The second frequency band signal may comprise frequencies of the incoming sound between <NUM> and <NUM>, such between <NUM> and <NUM>, for example obtained by filtering the incoming sound signal by a second, or high-band, filter configured with appropriate cut-off frequencies, e.g. a lower cut-off frequency of <NUM> and upper cut-off frequency of <NUM>. Hence, the second, or high-band, filter preferably possesses a bandpass frequency response, but may alternatively merely possess a highpass filter response for example depending on high-frequency response characteristic of the microphone arrangement which supplies the microphone signal.

According to one embodiment of the present method of detecting speech of incoming sound, the plurality of separate frequency bands comprises a third, or mid-band, filter with a frequency response situated in-between the respective frequency responses of the first and second frequency bands. The mid-band filter is configured to generate a third, or mid-frequency, band signal based on the microphone signal. The mid-frequency band filter may for example possess a bandpass response such that the mid-frequency band signal comprise frequencies between <NUM> and <NUM> such as between <NUM> and <NUM> by appropriate configuration or selection of lower cut-off and upper cut-off frequencies following the above-mentioned designs. The latter embodiment may utilize the third frequency band signal to determine a third power envelope signal of the third frequency band signal, determining a third noise power envelope and third clean power envelope of the first power envelope signal and determining a third power envelope ratio based on the third noise power and clean power envelopes.

The skilled person will understand that the first frequency band signal preferably comprises dominant frequencies of voiced or plosive speech onsets via the frequency response of the low-band filter while dominant frequencies of unvoiced speech onsets are suppressed or attenuated for example by more than <NUM> dB or <NUM> dB. The second frequency band signal preferably comprises dominant frequencies of unvoiced speech onsets via the frequency response of the highband filter while dominant frequencies of voiced or plosive speech onsets are suppressed or attenuated - for example by more than <NUM> dB or <NUM> dB. If present, the mid-frequency band signal preferably contains a frequency range or region with least dominant speech harmonics.

The determination of the onsets of voiced speech in the first frequency band signal may be based on a first crest value or factor representative of a relative power or energy between the first clean power signal and the first stationary noise power signal. The first crest value may for example be obtained by dividing the first clean power signal and first stationary noise power signal. The determination of onsets of unvoiced speech in the second frequency band signal may be based on a second crest value representative of a relative power or energy between the second clean power signal and second stationary noise power signal. The second crest value may for example be determined by dividing the second clean power signal and second stationary noise power signal as discussed in additional detail below with reference to the appended drawings.

The first stationary noise power signal may be exploited to provide an estimate of a background noise level of the first frequency band signal and the second stationary noise power signal may similarly be exploited to provide an estimate of a background noise level of the second frequency band signal and so forth for the optional third band signal. The first stationary noise power signal or estimate may comprise or be a so-called "aggressive" stationary noise power signal or estimate and/or the second stationary noise power signal may comprise a so-called "aggressive" stationary noise power signal or estimate that are determined or computed as discussed in additional detail below with reference to the appended drawings.

The first and second non-stationary noise power signals or estimates may be exploited to provide respective estimates of the non-stationary noise in the first and second frequency band signals, respectively, and may be determined or computed as discussed in additional detail below with reference to the appended drawings.

The determination of the first power envelope signal or estimate may comprise:.

The non-linear averaging of the each of the first and second frequency band signals may be viewed as applying these signals to the inputs of respective lowpass filters which exhibit one forgetting factor, i.e. corresponding to the attack time, if or when the frequency band signal exceeds an output of the lowpass filter and another forgetting factor, i.e. corresponding to the release time, when the frequency band signal is smaller than the filter output as discussed in additional detail below with reference to the appended drawings.

One embodiment of the present method comprises determination of a first fast onset probability, fastOnsetProb_1, of the first frequency band signal by comparing the first crest value with predefined minimum and maximum threshold values - for example according to:
fastOnsetProb_1 = min(<NUM>, max(<NUM>, (crest - crestThIdMin) / (crestThidMax - crestThldMin))). The latter embodiment may additionally, or alternatively, comprise:.

When the first fast onset probability reaches a value of one the speech detector may take this condition as a direct indication of the onset of voiced speech in the first frequency band signal or alternatively, the speech detector may utilize this condition to apply further test(s) to the first power envelope signal, or its derivative signals, before indicating, or not indicating, the onset of voiced speech depending on the outcome of these further test(s). Likewise, in response to the second fast onset probability reaches a value of one the speech detector may take this condition as a direct indication of the onset of unvoiced speech in the second frequency band signal, or alternatively, the speech detector may utilize the latter condition to apply further test(s) to the second power envelope signal, or its derivative power signals, before indicating, or not indicating, the onset of unvoiced speech depending on the outcome of these further test(s).

The speech detector and present methodology may utilise a duration of the fast onset of the first frequency band signal and/or a duration of the fast onset of the second frequency band signal as criteria for determining whether the fast onset in question is a reliable, or statistically significant, indicator, of the presence of voiced speech onsets or unvoiced speech in the incoming sound and the microphone signal. If the duration of the fast onset of the first or second frequency band signal is less than a predetermined time period such as <NUM> (<NUM>) the fast onset may be categorized as an impulse sound and the value of the speech probability estimator maintained or decreased.

Certain embodiments of the present methodology of detecting speech which determine the durations of the fast onsets in the first and/or second frequency band signals and therefore may further comprise:.

Certain embodiments of the present methodology of detecting speech check or monitor the power of the first and second clean power signals, as derived from the first and second frequency band signals, respectively, and may therefore further comprise:.

The speech detector may likewise be configured to indicate occurrence of a fast onset in the second frequency band signal in response to the second fast onset probability, fastOnsetProb_1, reaches a value of one,.

One embodiment of the present method of detecting speech and corresponding speech detector further comprises:.

In contrast, in case the multiple fast onsets are not indicated concurrently in the first and second frequency band signals:.

One embodiment of the present method of detecting speech and a corresponding speech detector further comprises:.

The latter embodiment is therefore helpful to further distinguish between e.g. speech like low-frequency dominant noise in the received microphone signal true voiced speech in the microphone signal because a fast onset in the low-frequency (first) band signal rarely or never is accompanied by a fast onset in the high-frequency (second) frequency band signal concurrently, or close thereto, in time due the temporal characteristics of human speech. Hence, the latter embodiments avoid that the speech detector and methodology by mistake indicate or flag speech like low-frequency dominant noise as voiced speech onsets.

The method of detecting speech may further comprise:.

A second aspect of the invention relates to a speech detector configured, adapted or programmed to receive and process the microphone signal, or its derivatives such as one or more of the first and second frequency band signals, the first and second power envelope signals, the first and second stationary noise power signals, the first, second clean power signals etc., in accordance with any of the above-described methods of detecting speech. The speech detector may be executed or implemented by dedicated digital hardware on a digital processor or by one or more computer programs, program routines and threads of execution running on a software programmable digital processor or processors or running on a software programmable microprocessor. Each of the computer programs, routines and threads of execution may comprise a plurality of executable program instructions that may be stored in non-volatile memory of a head-wearable communication device. Alternatively, the audio processing algorithms may be implemented by a combination of dedicated digital hardware circuitry and computer programs, routines and threads of execution running on the software programmable digital signal processor or microprocessor. The software programmable digital processor, microprocessor and/or the dedicated digital hardware circuitry may be integrated on an Application Specific Integrated Circuit (ASIC) or implemented on a FPGA device.

A third aspect of the invention relates to a portable device such as a head-wearable communication device for example a hearing aid, hearing instrument, active noise suppressor or headset, comprising:.

The hearing aid may be a BTE, RIE, ITE, ITC, CIC, RIC, IIC etc. type of hearing aid which comprises a housing shaped and sized to be arranged at, or in, the user's ear or ear canal.

In the following various exemplary embodiments of the present speech detector and corresponding methodology of detecting speech in incoming sound are described with reference to the appended drawings. The skilled person will understand that the accompanying drawings are schematic and simplified for clarity and therefore merely show details which are essential to the understanding of the invention, while other details have been left out. Like elements will, thus, not necessarily be described in detail with respect to each figure.

<FIG> is a schematic block diagram of a head-wearable communication device <NUM>, for example a hearing aid, hearing instrument, active noise suppressor or headset etc., comprising a speech detector <NUM> in accordance with an exemplary embodiment of the invention. The head-wearable communication device <NUM> comprises a microphone arrangement which comprises at least one microphone and preferably comprises first and second omnidirectional microphones <NUM>, <NUM> that generate first and second microphone signals, respectively, in response to incoming or impinging sound. Respective sound inlets or ports (not shown) of the first and second omnidirectional microphones <NUM>, <NUM> may be arranged with a certain spacing in a housing portion (not shown) of the head-wearable communication device <NUM> so as to enable the formation of the various types of beamformed microphone signals.

The head-wearable communication device <NUM> preferably comprises one or more analogue-to-digital converters (A/Ds) <NUM> which convert analogue microphone signals into corresponding digital microphone signals with certain resolution and sampling frequency before inputted to a software programmable, or hardwired, microprocessor or DSP <NUM> of the head-wearable communication device <NUM>. The software programmable, DSP <NUM> comprises or implements the present speech detector <NUM> and the corresponding methodology of detecting speech. The skilled person will understand that the speech detector <NUM> may be implemented as dedicated computational hardware of the DSP <NUM> or implemented by a set of suitably configured executable program instructions executed on the DSP <NUM> or by any combination of dedicated computational hardware and executable program instructions. The operation of the head-wearable communication device <NUM> may be controlled by a suitable operating system executed on the software programmable DSP <NUM>. The operating system may be configured to manage hardware and software resources of the head-wearable communication device <NUM>, e.g. including peripheral device, I/O port handling and determination or computation of the below-outlined tasks of the speech detector etc. The operating system may schedule tasks for efficient use of the hearing aid resources and may further include accounting software for cost allocation, including power consumption, processor time, memory locations, wireless transmissions, and other resources.

If the head-wearable communication device <NUM> comprises, or implements, a hearing aid it may additionally comprise a hearing loss processor (not shown). This hearing loss processor is configured to compensate a hearing loss of a user of the hearing aid. The hearing loss compensation may be individually determined for the user via well-known hearing loss evaluation methodologies and associated hearing loss compensation rules or schemes. The hearing loss processor may for example comprises a well-known dynamic range compressor circuit or algorithm for compensation of frequency dependent loss of dynamic range of the user of the device. The digital microphone signal or signals are applied to an input <NUM> of the speech detector <NUM> which in response outputs a speech flag or marker <NUM> which indicate speech in the incoming sound to the DSP <NUM> for example via a suitable input port of the DSP <NUM>. The DSP may therefore use the speech flag to adjust or optimizes values of various types of signal processing parameters as discussed above. The DSP <NUM> generates and outputs a processed microphone signal to a D/A converter <NUM>, which preferably may be integrated with a suitable class D output amplifier, before the processed output signal is applied to a miniature loudspeaker or receiver <NUM>. The loudspeaker or receiver <NUM> converts the processed output signal into a corresponding acoustic signal for transmission into the user's ear canal.

The speech detector <NUM> comprises a filter bank <NUM> which is configured to divide or split the digital microphone signal into a plurality of separate frequency band signals <NUM>, <NUM>, <NUM> via respective frequency selective filter bands. The skilled person will appreciate that the filter bank <NUM> in alternative embodiments may be external to the speech detector and merely the relevant output signals of the filter bank routed into the speech detector. The plurality of separate frequency band signals <NUM>, <NUM>, <NUM> preferably at least comprises a first frequency band signal <NUM>, e.g. low-frequency band signal, suitable for detecting onsets of voiced speech and a second frequency band signal <NUM>, e.g. high-frequency band signal, suitable for detecting onsets of unvoiced speech. The plurality of separate frequency band signals <NUM>, <NUM>, <NUM> may additionally comprise a third frequency band <NUM>, or mid-frequency band signal <NUM>, situated in-between the first and second frequency bands. The skilled person will appreciate that the filter bank <NUM> may comprise a frequency domain filter bank, e.g. FFT based, or a time domain filter bank for example based on FIR or IIR bandpass filters.

One embodiment of the filter bank <NUM> comprises a so-called WARP filter bank as generally disclosed by the applicant's earlier patent application <CIT>. The frequency domain transformation, e.g. FFT, of the digital microphone signal is computed on a warped frequency scale results in numerous desirable properties such as minimal time delay as the direct signal path contains only a short input buffer and the FIR compression filter. Other noticeable advantages are absence of aliasing and a natural log-scale of the analysis frequency bands conforming nicely to the Bark based frequency scale of human hearing. <FIG> illustrates <NUM> separate frequency bands provided by an exemplary embodiment of the WARP filter bank <NUM>. The low-frequency band signal <NUM> may be obtained by summing outputs of several of the warped filters for example bands <NUM>, <NUM> and <NUM> such that the low-frequency band signal <NUM> comprises frequencies of the incoming sound between about <NUM> - <NUM>, more preferably between <NUM> - <NUM>. Adjacent frequencies are attenuated according to the roll-off rate or steepness of the warped bands. The high-frequency band signal <NUM> may be obtained by summing outputs of several of other of the warped filter bands for example bands <NUM>, <NUM> and <NUM> such that the high-frequency band signal <NUM> comprises frequencies of the incoming sound between about <NUM> - <NUM> such between <NUM> - <NUM>. The optional mid-frequency band signal <NUM> may comprise frequencies between <NUM> - <NUM> such between <NUM> - <NUM> and obtained by summing outputs of the warped bands <NUM>, <NUM> and <NUM>. The skilled person will appreciate that the splitting of the digital microphone signal into the above-outlined separate low-frequency, high-frequency and mid-frequency bands ensures that the low-frequency band contains dominant frequencies of voiced/plosive speech onsets while the high-frequency band contains dominant frequencies of unvoiced speech. The mid-frequency band preferably contains the frequency range or region with the least dominant speech harmonics.

The speech detector <NUM> additionally comprises respective signal envelope detectors <NUM> for the low-frequency band signal <NUM>, mid-frequency band signal <NUM> and high-frequency band signal <NUM> to derive or determine respective power envelope signals as discussed in additional detail below. The speech detector <NUM> further comprises three noise estimators or detectors <NUM> that derive various noise power envelopes, clean power envelopes and certain envelope ratios from each of the power envelope signals as discussed in additional detail below. Outputs of the three noise estimators or detectors <NUM> are inputted to respective fast onset detectors <NUM> that monitors the presence the fast onsets across the low-frequency, mid-frequency and high-frequency bands. The latter results are applied to respective inputs of a fast onset distribution detector <NUM>. The computed fast onset distributions are finally applied to a probability estimator <NUM> which is configured to increase or decrease a value of a speech probability and on that basis flag or indicate to the DSP <NUM> the presence of speech in the incoming sound as discussed in additional detail below.

<FIG> shows a schematic block diagram of various intermediate signal processing functions or steps, in particular estimation or determination of certain envelope ratios, carried out by the speech detector <NUM> on each of the low-frequency band signal <NUM>, mid-frequency band signal <NUM> and the high-frequency band signal <NUM>. In step <NUM>, the DSP <NUM> extracts, computes or determines a low-frequency, or first, power envelope or power envelope signal <NUM> of the frequency band signal in question, e.g. the low-frequency band signal <NUM>. The first power envelope signal <NUM> may for example be determined by performing non-linear averaging of the first frequency band signal <NUM> in step/function <NUM> - for example by lowpass filtering the first frequency band signal <NUM> using an attack time between <NUM> and <NUM> and a release time between <NUM> and <NUM> such as between <NUM> and <NUM>.

This non-linear averaging may be viewed as lowpass filtering using a lowpass filter with one forgetting factor, i.e. corresponding to the attack time, if or when the first frequency band signal <NUM> exceeds an output of the lowpass filter and another forgetting factor, i.e. corresponding to the release time, when the first frequency band signal <NUM> is smaller than the filter output (release). This non-linear averaging can more generally be stated as:
When x is the input signal of the non-linear averaging and s is the output signal of the non-linear averaging: <MAT> <MAT> <MAT> <MAT>.

The transformation from attack time and release time to variables p. ffAtt and p. ffRel, respectively, is given by: <MAT> <MAT>.

Where fs is the sampling time of the input signal and * denotes multiplication.

The DSP <NUM> additionally extracts, computes or determines a high-frequency, or second, power envelope signal of the high-frequency band signal <NUM> in a corresponding manner and may be using identical, or alternatively somewhat shorter, attack and release times in view of the higher frequency components or content of the high-frequency band signal <NUM>. The latter times may comprise an attack time between <NUM> and <NUM> and a release time between <NUM> and <NUM>. The DSP <NUM> may optionally extract, compute or determine a mid-frequency, or third, power envelope signal of the mid-frequency band signal <NUM> in a corresponding manner and may be using identical or somewhat shorter attack and release times for the non-linear averaging of the mid-frequency band signal <NUM> compared to those of the low-frequency band signal <NUM>.

During step <NUM>, the DSP <NUM> extracts, computes or determines various power envelope signals that are utilized for detection or identification of certain fast speech onsets within each of the low-frequency band, high-frequency band and mid-frequency band. The DSP <NUM> extracts, computes or determines a so-called low-frequency, or first, stationary noise power signal based on the low-frequency power envelope signal. The DSP <NUM> additionally extracts, computes or determines a high-frequency, or second, stationary noise power signal based on the high-frequency power envelope signal in a corresponding manner. The DSP <NUM> may finally extract, compute or determine a mid-frequency, or third stationary noise power signal based on the mid-frequency power envelope signal in a corresponding manner. This process or mechanism is schematically illustrated on <FIG> where the DSP in step/function <NUM> carries out computation of the low-frequency, high-frequency and mid-frequency stationary noise power signals <NUM> based on the respective ones of the low-frequency, high-frequency and mid-frequency power envelope signals <NUM> provided by step/function <NUM>. The computation of these low-frequency, high-frequency and mid-frequency stationary noise power signals <NUM> serve to provide an accurate estimate of the background noise power level in, or of, the incoming sound as represented by the digital microphone signal or signals. Each of the low-frequency, high-frequency and mid-frequency stationary noise power signals <NUM> may comprise an aggressive stationary noise power signal <NUM> as discussed below in additional detail.

Overall, the speech detector <NUM> may be configured to determine the aggressive stationary noise power signals <NUM> (stn estimates) for the corresponding power envelope signals <NUM> as schematically illustrated by a signal flowchart <NUM> of <FIG>, by:.

The skilled person will understand that the stationary noise power signal or estimate estimates a noise floor of incoming sound within the frequency band signal in question. Hence, the stationary noise power signal can be understood as tracking a minimum noise power in the relevant frequency band signal. The present aggressive stationary noise signal or estimate <NUM> fluctuates markedly more than a traditional stationary noise power estimate. The present aggressive stationary noise signal or estimate <NUM> is configured to estimate power of the power envelope signal <NUM> just before an increase in power to estimate power of a new onset as discussed in additional detail below in connection with the computation of the non-stationary noise power signal <NUM>.

An exemplary code to implement the steps of the signal flowchart <NUM> follows here:
<IMG>
<IMG>.

All states are preferably initialized at zero.

The computations of the crest and cleanPowEnv variables are outlined in detail below.

Reverting to <FIG>, the speech detector <NUM> proceeds by function <NUM> to subtract the aggressive stationary noise power signal <NUM> from the power envelope signal <NUM> to generate the above-mentioned power envelope signal without stationary noise <NUM> (stnEstPowEnv) in each of the frequency bands. The power envelope signal without stationary noise <NUM> may be viewed as the frequency band signal in question cleaned from stationary noise. As illustrated by the signal flowchart of <FIG>, the power envelope signal without, i.e. cleaned from, stationary noise <NUM> is applied to the input of a block/function <NUM> which additionally extracts, computes or determines the so-called low-frequency, or first, non-stationary noise power signal or estimate <NUM>. The speech detector <NUM> additionally extracts, computes or determines a high-frequency, or second, non-stationary noise power signal or estimate <NUM> based on the high-frequency power envelope signal <NUM> in a corresponding manner and optionally computes a mid-frequency, or third, non-stationary noise power signal <NUM> based on the mid-frequency power envelope signal <NUM> in a corresponding manner.

The respective roles of the aggressive stationary noise power signal <NUM>, non-stationary noise power signal or estimate <NUM> and clean power signal or estimate <NUM> of a particular frequency band signal may be understood by considering a frequency band signal, derived from the incoming sound, which includes a mixture of sound sources comprising a stationary noise source, a non-stationary noise source and target speech. In that common sound situation the stationary noise power signal indicates or tracks the noise floor of the frequency band signal and, hence, a true stationary noise power. This true stationary noise power also corresponds to a minimum value of the aggressive stationary noise power signal <NUM>. When the frequency band signal, and the corresponding power envelope signal <NUM>, comprises or encounters a non-stationary noise "jump" or "bump", an ordinary stationary noise power estimate will remain substantially constant and not influenced by the non-stationary noise "jump" or "bump". In contrast, the present aggressive stationary noise power signal <NUM> will, after the onset of the non-stationary noise "jump" or "bump" has died out become equal to a total noise in the frequency band signal. Now assume that a speech onset takes place after the non-stationary noise "jump" or "bump" has died out. The best estimate of the power of that speech onset is obtained by a difference of the power of the frequency band signal just before the speech onset, which was tracked by the aggressive stationary noise power signal <NUM>, and the power after the speech onset has died out. So the aggressive stationary noise power signal <NUM> provides the speech detector with an estimate of the total power increase of the frequency band signal caused by each new jump in power.

Each of the non-stationary noise power signals <NUM> may be determined or computed by block <NUM> of the speech detector using signal processing steps schematically illustrated on the flowchart on <FIG>. In step <NUM>, the speech detector <NUM> defines a variable stnRemovedPowerEnvelope = power envelope signal <NUM> minus (-) aggressive stationary noise power signal <NUM>; In step <NUM>, in response to the value of stnRemovedPowerEnvelope exceeds the non-stationary noise power signal <NUM>, the speech detector jumps to step <NUM>. In step <NUM> an estimated increase in the non-stationary noise power signal or estimate <NUM> is set equal to a forgetting factor times the power envelope signal <NUM> minus the aggressive stationary noise power signal <NUM>; where the forgetting factor corresponds to a settling time of about <NUM> to <NUM> msec. Further in step <NUM>, the non-stationary noise power signal <NUM> (nstn estimate) is set equal to
max(<NUM>, min(stnRemovedPowerEnvelope minus stnRemovedPowerEnvelopePrev, the non-stationary noise power signal <NUM> + estimated increase (delta) in the non-stationary noise power signal <NUM>));.

In step <NUM>, the clean power signal or estimate <NUM> is determined as the power envelope signal <NUM> minus the aggressive stationary noise power signal <NUM> minus the non-stationary noise power signal <NUM> as depicted on <FIG>.

In step <NUM>, in response to the value of stnRemovedPowerEnvelope is smaller than the non-stationary noise power signal <NUM>, the speech detector jumps to step <NUM> wherein the non-stationary noise power signal or estimate <NUM> (nstn) is set equal to the value of stnRemovedPowerEnvelope; the speech detector proceeds to step <NUM> and determines the clean power signal or estimate <NUM> as the power envelope signal <NUM> minus the aggressive stationary noise power signal <NUM>, corresponding to signal <NUM> and from latter subtracts the non-stationary noise power signal or estimate <NUM> as depicted on <FIG> if the optional down-slope smoothing function <NUM> is disregarded or omitted as discussed below.

An exemplary code snippet to implement block <NUM> to compute or determine the non-stationary noise power signal <NUM> according to the signal flowchart of <FIG> follows here:
<IMG>.

All states or variables are preferably initialized at zero.

In summary, for each frequency band signal, the associated clean power signal <NUM> is generated by subtracting the associated aggressive stationary noise power signal <NUM> and the, optional, associated non-stationary noise power signal <NUM> from the power envelope signal <NUM>. The computation of these non-stationary noise power signals is optional but may serve to obtain accurate estimates of the first, second and third clean power signals <NUM> and ultimately increase the accuracy of the speech detection.

The speech detector <NUM> is configured or programmed to proceed by computing certain peak-to minimum power envelope factors or ratios in the low-frequency, mid-frequency and high-frequency bands. The speech detector preferably exploit one or more of these peak-to minimum power envelope ratios power envelope ratios to identify or indicate voiced speech onsets and unvoiced speech onsets in the incoming sound. More specifically, the speech detector <NUM> is preferably configured to, in step <NUM>, determine the low-frequency power envelope ratio by determining a low-frequency, i.e. first, crest factor or ratio <NUM> using the crest block or function <NUM> by dividing the low-frequency clean power signal <NUM> and low-frequency aggressive stationary noise power signal <NUM>. The low-frequency crest ratio <NUM> = crest is preferably determined by estimating a peak-to-minimum power envelope ratio or value between the low-frequency clean power signal <NUM> and low-frequency aggressive stationary noise power signal <NUM>, i.e. <MAT>.

The speech detector <NUM> may be configured to compute high-frequency and mid-frequency crest ratios <NUM> in a corresponding manner based on the respective high-frequency and mid-frequency clean power signals <NUM> and aggressive stationary noise power signals <NUM>. The skilled person will appreciate that each of the crest ratios <NUM> may be indicative of a peakiness of the corresponding power envelope signal <NUM> after removal of all stationary noise components and non-stationary noise components.

<FIG> illustrates the results of the above-mentioned power envelope determinations in the low-frequency band for an exemplary noisy speech signal over a time span or segment of about <NUM>. Plot <NUM> is the determined low-frequency power envelope signal, plot <NUM> is the low-frequency aggressive stationary noise power signal, plot <NUM> is the low-frequency non-stationary noise power signal and finally, plot <NUM> is the corresponding low-frequency clean power signal <NUM>. It is evident that the low-frequency clean power signal <NUM> largely only contains fast envelope power jumps or fluctuations.

<FIG> is a schematic flow chart of signal processing steps carried out by an exemplary embodiment of the fast onset detectors <NUM> of the speech detector <NUM> (refer to <FIG>) executed on the DSP to compute a speech probability estimator based on indications of voiced speech onsets and unvoiced speech onsets in the low-frequency and high-frequency bands, respectively. The speech detector <NUM> utilizes the above-discussed low-frequency, high-frequency and optionally the mid-frequency power envelope signals <NUM>, the low-frequency, high-frequency and mid-frequency aggressive stationary noise power signals <NUM>, the low-frequency, high-frequency and mid-frequency non-stationary noise power signals <NUM> and the low-frequency, high-frequency and mid-frequency clean power signals <NUM>.

In step or function <NUM> the speech detector <NUM> initially determines a low-frequency, or first, fast onset probability, fastOnsetProb_1, associated with the low-frequency band signal based on the crest ratio <NUM> of that frequency band. The speech detector may for example determine a fast onset probability by setting variable fastOnsetProb:
fastOnsetProb = min(<NUM>, max(<NUM>, (crest - crestThldMin) / (crestThidMax - crestThldMin))); where typical values for crestThIdMin lie between <NUM> and <NUM> and for crestThldMax lie between <NUM> and <NUM>;.

Also, the two following states, which are used in determination of the clean power msignal <NUM>, are reset based on the determined crest factor <NUM>:
if crest > crestPowEnvMaxDurFastOnsetRel
cntAggrStnEstAttackTimeOut = cntAggrStnEstAttackTimeOutlnit;
powEnvAggrMinTracker = power envelope signal <NUM> (powEnv);
end.

In step <NUM> the speech detector <NUM> preferably additionally determines corresponding high-frequency and/or mid-frequency fast onset probabilities using similar thresholding mechanisms as outlined above. According to the inventors' experimental data, the threshold value crestThIdMin may lie between <NUM> and <NUM> and the value of threshold crestThIdMax may lie between <NUM> and <NUM>. The respective values of crestThIdMin and crestThIdMax may vary between the low-frequency, high-frequency and mid-frequency bands or may be substantially identical across these frequency bands. The specific threshold values may in some embodiments lie between <NUM> and <NUM> in the low-frequency band and <NUM> and <NUM> in the mid-frequency band and high-frequency band.

In response to a fast onset detection in one of the power envelope signals <NUM>, the variable fastOnsetProb_1 of the low-frequency band, mid-frequency band or high-frequency band, as the case may be, is set a value of one (<NUM>). The fast onset may be flagged or categorized as a fast onset directly in response to the variable fastOnsetProb_1 is one or may alternatively be subjected to further tests before the fast onset is categorized as an onset of voiced speech in the incoming sound or as an onset of unvoiced speech in the incoming sound. The speech detector <NUM> may during processing step <NUM> for example categorize the fast onset as an impulse sound, as opposed to speech sound or component, if multiple fast onsets are detected concurrently in the low-frequency and high-frequency power envelope signals <NUM>. Likewise, the speech detector <NUM> may in function or step <NUM> categorize the fast onset as an impulse sound, as opposed to speech sound or component, if the duration of each of the multiple fast onsets is less than a predetermined time period, or duration threshold, such as <NUM> (<NUM>). This is because it is a priori known that typical voiced speech components have longer duration than the duration threshold. If one or both of these criteria are fulfilled, the detected fast onset may safely be categorized as impulse sound or sounds and the speech detector <NUM> may accordingly decrease the value of the speech probability estimator <NUM> via the illustrated connection or wire <NUM>.

In contrast, when the speech detector categorizes a particular fast onset as not an impulse sound, the speech detector <NUM> may categorize the fast onset as a voiced speech onset on the condition multiple fast onsets mainly are detected in the low-frequency power envelope signal <NUM> and increase the value of the speech probability estimator <NUM>. The speech detector <NUM> may categorize the fast onset as a probable onset of unvoiced speech if the multiple fast onsets are mainly detected in the high-frequency power envelope signal and/or mainly detected in the mid-frequency power envelope signal and increase the value of the speech probability estimator <NUM>.

As an alternative, or possibly additionally, criterion the speech detector <NUM> may categorize the fast onset as a voiced speech onset on the condition that the power or energy of the low-frequency clean power signal following the fast onset is significantly larger, e.g. at least <NUM> to <NUM> times larger, than the power or energy of the high-frequency clean power signal following the fast onset. The processing step or function <NUM> of the speech detector enables the speech detector <NUM> to make that determination by tracking or computing the respective maximum clean powers of the low-frequency, high-frequency and mid-frequency clean power signals <NUM> following a fast onset in any of the frequency bands. The speech detector <NUM> preferably exclusively increases the value of the speech probability estimator <NUM> if that latter criterion/condition is fulfilled.

In a similar manner, the speech detector <NUM> may categorize a fast onset in the high-frequency band signal as an unvoiced speech onset on the condition that the power or energy of the high-frequency clean power signal following the fast onset is significantly larger than the power or energy low-frequency clean power signal. Optionally in addition larger than the power or energy of the mid-frequency clean power signal, following the fast onset. The speech detector <NUM> preferably only increases the value of the speech probability estimator <NUM> via the illustrated connection or wire <NUM> in response to compliance with the latter criterion/condition.

If neither condition is fulfilled for the particular fast onset, the speech detector <NUM> preferably decreases the value of the speech probability estimator <NUM> via the illustrated input variable over wire <NUM>. The output <NUM>, of the speech detector <NUM>, please refer to <FIG>, may be configured to indicate or flag presence of speech in the incoming sound, i.e. speech = Y or speech = N at any particular time instant, by suitable adaptation of the speech probability estimator <NUM>. The speech probability estimator <NUM> complies with a certain, or pre-set, speech criterion such as a value of the speech probability estimator exceeds a predetermined threshold. As schematically illustrated by <FIG>, the DSP <NUM> may use the speech flag or signal <NUM> to adjust one or more parameters of one or several signal processing algorithm(s), for example the previously discussed environmental classifier algorithm, noise reduction algorithm, speech enhancement algorithm etc., executed on the portable communication device by the DSP <NUM>.

Claim 1:
A method of detecting speech of incoming sound at a portable communication device, comprising:
- generate a microphone signal by a microphone arrangement of the portable communication device in response to the incoming sound,
- divide the microphone signal into a plurality of separate frequency band signals comprising at least a first frequency band signal suitable for detecting onsets of voiced speech and a second frequency band signal suitable for detecting onsets of unvoiced speech,
- determine a first power envelope signal of the first frequency band signal and a second power envelope signal of the second frequency band signal,
- deriving a first stationary noise power signal and first non-stationary noise power signal from first power envelope signal,
- derive a first clean power signal by subtracting the first stationary noise power signal and the first non-stationary noise power signal from the first power envelope signal,
- derive a second stationary noise power signal and second non-stationary noise power signal from second power envelope signal,
- derive a second clean power signal by subtracting the second stationary noise power signal and the second non-stationary noise power signal from the second power envelope signal,
- determine onsets of voiced speech in the first frequency band signal based on the first stationary noise power signal and first clean power signal,
- determine onsets of unvoiced speech in the second frequency band signal based on the second stationary noise power signal and second clean power signal,
- increasing or decreasing a value of a speech probability estimator based on determined onsets of voiced speech and determined onsets of unvoiced speech.