APPLICATIONS IN SPEECH

ENHANCEMENT

BY

BHARATH V 1BM11TE012

PRAVEEN D S 1BM11TE038

SANDEEP K M 1BM11TE046

SHRISHA UDUPA S 1BM11TE052

DEPT OF TCE, BMSCE

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UNDER THE GUIDANCE OF

PRASANNA KUMAR M K

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INTRODUCTION

SPEECH ENHANCEME

NT

Aims to improve the speech quality by using various algorithms

Clarity and intelligibilit

y

Pleasantness

Compatibility with some

other method in

speech processing

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Central Methods for enhancing speech

• Removal of background noise• Echo Suppression• Process of artificially bringing certain frequencies into the speech signal

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Nosie reduction

and its importance

Used for many applications

such as mobile

phones,VoIP, teleconferencing systems,

speech recognition and hearing

aids

Implementation and

evaluation of the various

speech enhancement techniques on

signals degraded by

noise

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PROJECT GOALS

Examine the Spectral Subtraction speech enhancement technique and then simulate it in Matlab

Examine the Wiener Filtering method and simulate in Matlab

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BLOCK DIAGRAM

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• An audio signal which is a representation of sound, typically as electrical voltage

• Audio frequency range of 20Hz to 20kHz

• Speech signal, music signal or a single frequency signal

Input Signal

8• General term for unwanted

modifications that a signal may suffer during capture, storage, transmission, processing or conversion

• Additive noise, Burst noise, Shot noise

• Measured in absolute terms, relative to some standard noise level or relative to the desired signal level

Noise signal

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Windowing

• Process of taking a small subset of a larger dataset for processing and analysis

• Window function is a mathematical function that is zero-valued outside of some chosen interval

• Application include spectral analysis, filter design and beam forming

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Hamming window

• Helps us to reduce the spectral leakage from the signal which can occur when a rectangular window is used and can cause distortion or discontinuities in the signal

• The Hamming window is used in this project due to fact that 99.96 per cent of the spectral energy is contained within the main lobe of the window

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12FOURIER TRANSFORM

It decomposes a function of time into the sum of sinusoidal functions similar to how a musical chord can be expressed as the amplitude of its constituent notes

Converts a signal from time domain to frequency domain

Short Term Fourier Transform is used to determine the sinusoidal frequency and phase content of local section of a signal as it changes over time

Quicker and more efficient

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SPECTRAL SUBTRACTION

Spectral amplitude estimation method to restore the signals degraded by additive noise

Phase distortion can be ignored since human ear is insensitive to the phase

Restoring the signal by subtracting an estimate of the noise spectrum from the noisy signal spectrum

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Noise in the degraded speech is estimated from the ‘pauses’ or ‘quiet’ periods in the speech signal, when there is no speech being said and only noise is present

Takes place in Frequency domain and hence FFT is used

Speech signal is split up into overlapping frames of size N

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A Hamming window is applied to the signal to further reduce artifacts appearing in the signal due to the samples being processed twice

Finally the windows are added back together using an overlap of 50%

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17 Get the noise spectrum along with the signal

spectrum, then taking the noise from the degraded signal to get the cleaned speech signal

Noise vector of the same length as the original signal created using the ‘randn’ function in Matlab and added to the original signal so that it is degraded by additive noise

FFT is used to calculate the spectrum and ‘abs’ function is used to calculate the magnitude of degraded and noise signals

MagY = MagX – MagN

18RESULTS

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WEINER FILTERING

Using the FFT and applying the Hamming window along with the overlapping at 50% are same

Main difference is how they remove the noise from the degraded speech signal

Estimation of speech and noise power spectrum

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Frequency response is multiplied with the signal spectrum

W = MagX./(MagX +MagN) MagY = MagX .* W Spectral filter took away the noise the Wiener

works by suppressing it by multiplying the frequency response with the signal spectrum

23RESULTS

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25Spectral Subtraction

Weiner Filtering

Low noise

Mediumnoise

High noise

Low noise

Medium noise

High noise

Bharath 9 8 7 8 7 7

Darshan 8 8 6 9 7 8

Praveen 8 7 8 7 8 8

Sandeep 9 7 7 9 7 7

Shrisha 8 9 7 8 9 7

Milind 9 8 8 9 8 8

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APPLICATIONS

De noising Removing interference caused by other

speakers Separating vocals from music Automatic Gain Control

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CONCLUSION

Implementation and simulation using Matlab and comparison of the techniques employed to see which offered the greater detection and filtered speech

Objective testing of the two enhancement techniques using Signal to Noise Ratio

Using different listeners to analyze the above speech enhancement techniques

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The two filters both Spectral Subtraction and Wiener Filter are close at lower SNR

Very little difference between the two filters at this level of SNR

At higher SNR the Wiener filter seems to out perform the Spectral Subtraction

The Wiener Filter is the preferred form of filtering at the higher level of SNR

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FUTURE WORK

It is necessary for the speech enhancement techniques to be able to detect the noise in a signal automatically

The filters also need to update the noise signal since noise is so random that it can change during a speech signal

Use the above methods in real time analysis of speech signals

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REFERENCES

Saeed V. Vaseghi ‘Advanced Digital Signal Processing and Noise Reduction’ Third Edition (2005)

Joachim Thiemann: ‘Acoustic Noise Suppression for Speech Signals using Auditory Masking Effects’ ‘http://www-mmsp.ece.mcgill.ca/MMSP/Theses/2001/ThiemannT2001.pdf’ (2001)

F.J. Owens: ‘Signal Processing of Speech’ (1993) Kamil K. W´ojcicki, Benjamin J. Shannon and Kuldip K.

Paliwal ‘Spectral Subtraction with Variance Reduced Noise Spectrum Estimates’

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THANK YOU