Voice Activity Detection on TMS320C6713 DSK

SYNOPSIS

The emerging applications of speech technology especially in the fields of wireless applications,

digital hearing aids or speech recognition are often requiring a noise reduction technique in

combination with a precise Voice Activity Detector (VAD).

In this project, we will implement five different VAD algorithms and apply them individually in

a Multi Band Spectral Subtraction based Speech Enhancement System. The five different VAD

algorithms are SNR based VAD, Zero Crossing Detection based VAD, Entropy based, Modified

Entropy bsed VAD and MEL filter based VAD. The performances of the VAD algorithms are to

be evaluated using several Objective Measures such as Log-Likelihood Ratio, Segmental SNR,

Weighted Spectral Slope and Overall Speech Signal Distortion.

The performance comparison is to be done using the above mentioned Objectives Measures of

the MBSS based Speech Enhancement System employing the five different implementations

individually.

Based on the complexity, efficacy of the VAD algorithms and also keeping in view of real time

criticality, two out of five VAD implementations are chosen for the real time implementation of

MBSS- Speech Enhancement Systems on TMS320C6713 Starter Kit (DSK).

OVERVIEW / SCOPE OF WORK :

Conversational speech is a sequence of contiguous segments of speech and silence. In a two way

telephone conversation, one party is active for only about 35% of the time. This can be exploited

effectively for the reduction of the average bit rate and co-Channel interference in digital cellular

systems. The success of any scheme exploiting this property is critically dependent upon the

algorithm used for Voice Activity Detection (VAD), i.e, the processes of discrimination of

speech from silence or other background noise. VAD algorithms take recourse to some form of

speech pattern classification to differentiate between voice and silence periods. Thus identifying

and rejecting transmission of silence period helps to reduce internet traffic. VAD is used in a

variety of speech communications systems such as speech coding, speech recognition, hands free

telephony, audio conferencing, speech enhancement and echo cancellation.

The VAD is used for improving speech detection robustness in noisy environments and the

performance of the speech recognition systems. For stationary noise, averaging the spectrum of

the noisy signal during the initial silence period can often be sufficient. That is not the case,

however, with non-stationary noise since the noise spectrum will be varying rapidly over time.

To overcome this problem, the noise spectrum needs to be estimated and updated continuously.

This is a challenging task since we only have access to the noisy speech signal. Noise estimation

algorithms are therefore needed which can track the noise without explicitly doing speech/silence

detection.

The following are some of the required features of a good VAD algorithm:

1. Good Decision Rule: A physical property of speech that can be exploited to give

consistent and accurate judgment in classifying segments of the signal into silence or

otherwise.

2. Adaptability to background Noise: Adapting to non stationary background noise

improves the robustness, especially in wireless telephony.

3. Low Computational Complexity. The complexity of VAD algorithm must be low to suit

real-time applications.

The following are the algorithms based on which the VAD systems are to be implemented

on MATLAB:

Signal To Noise Ratio Based VAD

Zero Crossing Detection Based VAD

Entropy Based VAD

MEL Filtering based VAD

The VAD system that performs best when compared with others will be implemented on

TMS320C6713DSK

HARDWARE IMPLEMENTATION

The hardware implementation of the noise suppression system is to be implemented using done

using TMS320C6713 DSK which is shown as below:

The hardware board takes the speech signal through the ‘line in’ terminal of the DSK and is

processed by the DSP on the board, based on the detection of the speech signal in the input wave,

the on-board LEDs are made ON to indicate the presence of the speech signal.

TOOLS TO BE USED :

MATLAB Software

Visual Studio (2008)

Code Composer Studio 3.1

TMS320C6713 DSK