Browsing by Author "Yoganathan, Vaitheki"
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- ItemMulti-microphone speech enhancement technique using a novel neural network beamformer : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Albany, New Zealand(Massey University, 2014) Yoganathan, VaithekiThis thesis presents a novel speech enhancement algorithm to reduce the background noise from the acquired speech signal. It introduces an innovative idea for the speech beamformer using an input delay neural network based adaptive filter for noise reduction. Speech communication is considered as the most popular and natural way for humans to communicate with computers. In the past few decades, there has been an increased demand for speech-based applications; examples include personal dictation devices, hands-free telephony, voice recognition for robotics, speech-controlled equipment, automated phone systems, etc. However, these applications require a high signal-to-noise ratio to function effectively. The background noise sources such as factory machine noises, television, radio, computer or another competing speaker, often degrade the performance of the acquired signals. The problem of removing these unwanted signals from the acquired speech signal has been investigated by various authors. However, there is still room for improvement to the existing methods. A multi-microphone neural network based switched Griffiths-Jim beamformer structure was implemented using the Labview software. The conventional noise reduction section of the Griffiths and Jim beamformer structure was improved with a non-linear neural network approach. A partially connected three-layer neural network structure was implemented for rapid real-time processing. The error back-propagation algorithm was used here to train the neural network structure. Although it is a slow gradient learning algorithm, it can be easily replaced with other algorithms such as the fast back-propagation algorithm. The proposed algorithms show promising noise reduction improvement over the previous adaptive algorithms like the normalised least mean squares adaptive filter. However, the performance of the neural network depends on its chosen parameters such as learning rate, amount of training given, and the size of the neural network structure. Tests with a speech-controlled system demonstrate that the neural network based beamformer significantly improves the recognition rate of the system.
- ItemReal-time implementation of a dual microphone beamformer : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Computer Systems at Massey University, Albany, New Zealand(Massey University, 2005) Yoganathan, VaithekiThe main objective of this project is to develop a microphone array system, which captures the speech signal for a speech related application. This system should allow the user to move freely and acquire the speech from adverse acoustic environments. The most important problem when the distance between the speaker and the microphone increases is that often the quality of the speech signal is degraded by background noise and reverberation. As a result, the speech related applications fails to perform well under these circumstances. This unwanted noise components present in the acquired signal have to be removed in order to improve the performance of these applications. This thesis contains the development of a dual microphone beamformer in a Digital Signal Processor (DSP). The development kit used in this project is the Texas Instruments TMS320C6711 DSP Starter Kit (DSK). The switched Griffiths-Jim beamformer was selected as the algorithm to be implemented in the DSK. Van Compernolle developed this algorithm in 1990 by modifying the Griffiths-Jim beamformer structure. This beamformer algorithm is used to improve the quality of the desired speech signal by reducing the background noise. This algorithm requires atleast two input channels to obtain the spatial characteristics of the acquired signal. Therefore, the PCM3003 audio daughter card is used to access the two microphone signals. The software implementation of the switched Griffiths-Jim beamformer algorithm has two main stages. The first stage is to identify the presence of speech in the acquired signal. A simple Voice Activity Detector (VAD) based on the energy of the acquired signal is used to distinguish between the wanted speech signal and the unwanted noise signals. The second stage is the adaptive beamformer, which uses the results obtained from the VAD algorithm to reduce the background noise. The adaptive beamformer consists of two adaptive filters based on the Normalised Least Mean Squares (NLMS) algorithm. The first filter behaves like a beam-steering filter and it's only updated during the presence of speech and noise signal. The second filter behaves like an Adaptive Noise Canceller (ANC) and it is only updated when a noise alone period is present. The VAD algorithm controls the updating process of these NLMS filters and only one of these filters is updated at any given time. This algorithm was successfully implemented in the chosen DSK using the Code Composer Studio (CCS) software. This implementation is tested in real-time using a speech recognition system. This system is programmed in Visual Basic software using the Microsoft Speech SDK components. This dual microphone system allows the user to move around freely and acquire the desired speech signal. The results show a reasonable amount of enhancement in the output signal, and a significant improvement in the ease of using the speech recognition system is achieved.