e-book Noise reduction in speech processing

Free download. Book file PDF easily for everyone and every device. You can download and read online Noise reduction in speech processing file PDF Book only if you are registered here. And also you can download or read online all Book PDF file that related with Noise reduction in speech processing book. Happy reading Noise reduction in speech processing Bookeveryone. Download file Free Book PDF Noise reduction in speech processing at Complete PDF Library. This Book have some digital formats such us :paperbook, ebook, kindle, epub, fb2 and another formats. Here is The CompletePDF Book Library. It's free to register here to get Book file PDF Noise reduction in speech processing Pocket Guide.

The null is steered onto the speaker, by assuming that the source location is the point for which the output power of the negative beamformer is minimised. In this paper, the centre and left microphones, the centre and right microphones and the left and right microphones effectively form three sub-arrays. These sub-arrays are used to estimate the noise direction. The array nulls are then steered on to the speaker in order to obtain a noise estimate. This noise estimate is then subtracted from the noisy speech obtained from the central microphone using non-linear spectral subtraction. The technique is similar to that described in Alvarez et al However, the method of estimating the noise direction differs.

In Mizumachi and Akagi's paper, results are provided in terms of noise reduction, with a signal-to-noise SNR improvement of up to 6 dB being obtained.

Noise reduction - Wikipedia

A broadband sub-array delay sum beamformer is used to obtain the speech signal in their experiments. Furthermore, a signal-cancelling spatial notch filter is used to obtain the noise estimate. These beamformers are implemented using an array of nine microphones in a non-linearly spaced 40 cm broadside array. As known to those skilled in the art, this is a common Mel frequency warping technique that is applied to the spectral domain to convert signals into the Mel domain.

Significant improvements in speech recognition rate were reported for both localised and ambient noise sources. Notably, in this context, no beam-steering is employed; it is assumed that the speaker is directly in front of the array. In [5], both beamforming and directivity controlled arrays are examined, with the Wiener filter estimation being based on the spectrums from both array microphones.

Of note in [5] was the fact that the post-filter only provided an improvement when the array was effective, i. Also of note is the fact that the Wiener filter also provided no advantage if there was noise within the beam of the array or within a grating lobe. In another separate technique, sub-band Wiener filters have been used in conjunction with beam forming microphone arrays to produce an additional gain in SNR, as illustrated in [5] and [6]. In this case the Wiener filter coefficients are calculated using the coherence between the microphones.

However, this is only effective if the noise is spatially diffuse, which is not always the case. In order to calculate the coefficients of the Wiener filter an estimate of the noise is required. These estimates are taken during the gaps between the speech segments. The inventors have recognized and appreciated some limitations of this approach. In summary, such an approach concentrates on stationary noise. Hence, all of these techniques obtain the noise estimate just before the start of the speech, and then update the estimate in the speech-gaps, which is not ideal.

Thus, improving a noisy speech signal by more accurately estimating and removing background noise is a fundamental step in noise robust speech processing. However, by specifying the use of a Wiener filtering approach, the aforementioned Spectral subtraction techniques are effectively precluded from use.

Spectral subtraction and Wiener filtering are two different techniques that are independently used for noise robust speech recognition. They both essentially reduce the noise, but use different approaches. Thus, the two techniques cannot be used at the same time. In practice, this means that it is impossible to perform spectral subtraction using multiple microphones in conjunction with the Advanced Front End. A need therefore exists for an improved microphone array arrangement wherein the abovementioned disadvantages may be alleviated.

The present invention provides a communication or computing device, as claimed in claim 1 , a method for speech recognition in a speech communication or computing device, as claimed in claim 9 , and a storage medium, as claimed in claim Further features are as claimed in the dependent Claims. In summary, the present invention proposes to use a null beamforming microphone array to provide a substantially continuous noise estimate. This substantially continuous and therefore more accurate noise estimate is then used to adjust the coefficients of a Wiener Filter.

Advantageously, the proposed technique can be applied in any microphone array scenario where non-spatially diffuse noises exist. Embodiments of the present invention will now be described, by way of example only, with reference to the accompanying drawings, in which:. Referring now to FIG.

Nuance SSE

Although the present invention is described with reference to speech recognition in a wireless communication unit such as a third generation cellular device, it is within the contemplation of the invention that the inventive concepts can be equally applied to any speech-based device. As known in the art, the speech communication unit contains an antenna preferably coupled to a duplex filter or antenna switch that provides isolation between a receiver chain and a transmitter chain within the speech communication unit As also known in the art, the receiver chain typically includes receiver front-end circuitry effectively providing reception, filtering and intermediate or base-band frequency conversion.

The front-end circuit is serially coupled to a signal processing function An output from the signal processing function is provided to a suitable output device , such as a speaker via a speech-processing unit The speech-processing unit includes a speech encoding function to encode a user's speech signals into a format suitable for transmitting over the transmission medium.

The speech-processing unit also includes a speech decoding function to decode received speech signals into a format suitable for outputting via the output device speaker The speech-processing unit is operably coupled to a memory unit , via link , and a timer via a controller In particular, the operation of the speech-processing unit has been adapted to support the inventive concepts of the preferred embodiments of the present invention. The adaptation of the speech-processing unit is further described with regard to FIG.

For completeness, the receiver chain also includes received signal strength indicator RSSI circuitry shown coupled to the receiver front-end , although the RSSI circuitry could be located elsewhere within the receiver chain.

Navigation menu

The RSSI circuitry is coupled to a controller for maintaining overall subscriber unit control. The controller is also coupled to the receiver front-end circuitry and the signal processing function generally realised by a DSP. A timer is typically coupled to the controller to control the timing of operations transmission or reception of time-dependent signals within the speech communication unit Thereafter, any transmit signal is passed through a power amplifier to be radiated from the antenna Of course, the various components within the speech communication unit can be arranged in any suitable functional topology able to utilise the inventive concepts of the present invention.

Furthermore, the various components within the speech communication unit can be realised in discrete or integrated component form, with an ultimate structure therefore being merely an application-specific selection. It is within the contemplation of the present invention that the preferred use of speech processing and speech storing can be implemented in software, firmware or hardware, with the function being implemented in a software processor or indeed a digital signal processor DSP , performing the speech processing function, merely a preferred option.

More generally, it is envisaged that any re-programming or adaptation of the speech processing function , according to the preferred embodiment of the present invention, may be implemented in any suitable manner. For example, a new speech processor or memory device may be added to a conventional wireless communication unit Alternatively, existing parts of a conventional wireless communication unit may be adapted, for example, by reprogramming one or more processors therein.

Navigation menu

As such the required adaptation may be implemented in the form of processor-implementable instructions stored on a storage medium, such as a floppy disk, hard disk, programmable read-only memory PROM , random access memory RAM or any combination of these or other storage media. The speech recognition function has been adapted in accordance with a preferred embodiment of the present invention.

  • chapter and author info?
  • Completing the picture : fragments and back again?
  • ANF II: noise cancellation device | Speech Technology Center.
  • 1st Edition.
  • Becoming a Writing Researcher.
  • Auphonic Audio Examples?
  • Supervised Noise Reduction for Multichannel Keyword Spotting – Google AI.

A speech signal is input to a feature extraction function of the speech processing unit, in order to extract the speech characteristics to perform speech recognition. The feature extraction function preferably includes a speech frequency extension block , to provide a wider audio frequency range of signal processing to facilitate better quality speech recognition. The feature extraction function also preferably includes a voice activity detector function , as known in the art.

The input speech signal is input to a noise reduction function , which has been adapted in accordance with the preferred embodiment of the present invention, as described below with respect to FIG. In this way, the overall SNR is improved and also the speech periodicity is enhanced. The output from the waveform processing unit is input to a Cepstrum calculation block , which calculates the log, Mel-scale, cepstral features MFCC's. The output from the Cepstrum calculation block is input to a blind equalization function , which minimizes the mean square error computed as a difference between the current and target cepstrum.

This reduces the convolutional distortion caused by the use of different microphones in training of accoustic models and testing. The output from the blind equalization function , of the feature extraction function , is input to a feature compression function , which performs split vector quantisation on the speech features. The output from the feature compression function is processed by function , which frames, formats and incorporates error protection into the speech bit stream The speech signal is then ready for converting, as described above with respect to FIG.

The noise reduction block has been adapted in accordance with a preferred embodiment of the present invention. As illustrated in FIG. If this null is orientated towards the speaker, the output of the microphone will be the background noise. The plot illustrated in FIG. A second signal is obtained: either from a single microphone or a second microphone array not illustrated. In both cases the null is orientated directly away from the speaker, so that the output of the microphone or array S in n contains both speech and noise.

In accordance with the preferred embodiment, the output from the two microphones , is input to an array processing function in FIG. The array processing function subtracts the outputs of two cardioid microphones and to produce a noise estimate signal n n This facilitates the cleaning of strong transient noises such as noise created by passing cars while preserving the highest speech quality in quiet environments. Alango Noise Suppression technology includes some unique features such as tone suppression and tone preservation.

Tone suppression allows reducing strong periodic tonal noises. Tone preservation allows keeping the dial tone, DTMF or other tonal signals that are not noises and must be preserved. Besides noise suppression it includes acoustic echo cancellation , dynamic range compressor , automatic volume and equalization control , speech enhancement, optional adaptive dual microphone and several other functions. Voice Enhancement. Overall, the data from the present dissertation highlight the importance of preserving the acoustic landmarks present in the speech signal for improved speech understanding by cochlear implant users in noisy conditions.

Cochlear implants are prosthetic devices, consisting of implanted electrodes and a signal processor and are designed to restore partial hearing to the profoundly deaf community. Since their inception in early s cochlear implants have gradually gained popularity and consequently considerable research has been done to advance and improve the cochlear implant technology. Most of the research conducted so far in the field of cochlear implants has been primarily focused on improving speech perception in quiet.

Noise Reduction Software

Music perception and speech perception in noisy listening conditions with cochlear implants are still highly challenging problems. Many research studies have reported low recognition scores in the task of simple melody recognition. Most of the cochlear implant devices use envelope cues to provide electric stimulation. Understanding the effect of various factors on melody recognition in the context of cochlear implants is important to improve the existing coding strategies.

In the present work we investigate the effect of various factors such as filter spacing, relative phase, spectral up-shifting, carrier frequency and phase perturbation on melody recognition in acoustic hearing. The filter spacing currently used in the cochlear implants is larger than the musical semitone steps and hence not all musical notes can be resolved. Noise reduction methods investigated so far for use with cochlear implants are mostly pre-processing methods.

In these methods, the speech signal is first enhanced using the noise reduction method and the enhanced signal is then processed using the speech processor. A better and more efficient approach is to integrate the noise reduction mechanism into the cochlear implant signal processing.

Download this chapter in PDF format

SNR weighting noise reduction method is an exponential weighting method that uses the instantaneous signal to noise ratio SNR estimate to perform noise reduction in each frequency band that corresponds to a particular electrode in the cochlear implant. S-shaped compression technique divides the compression curve into two regions based on the noise estimate.

This method applies a different type of compression for the noise portion and the speech portion and hence better suppresses the noise compared to the regular power-law compression. A number of speech enhancement algorithms based on MMSE spectrum estimators have been proposed over the years. Although some of these algorithms were developed based on Laplacian and Gamma distributions, no optimal spectral magnitude estimators were derived. This dissertation focuses on optimal estimators of the magnitude spectrum for speech enhancement. We present an analytical solution for estimating in the MMSE sense the magnitude spectrum when the clean speech DFT coefficients are modeled by a Laplacian distribution and the noise DFT coefficients are modeled by a Gaussian distribution.

Furthermore, we derive the MMSE estimator under speech presence uncertainty and a Laplacian statistical model.