VLSI Implementation of an Adaptive Noise Canceller

Adaptive Noise cancelling is a speech-specific filtering technique to suppress additive interference. It is a sensible choice for speech-orientated devices in situations where the designer has no knowledge of the properties of interference that could potentially corrupt the input speech to a point beyond intelligibility. A good example of devices subject to such noise is mobile telephones, where the user may be communicating in a variety of environments. However, constraints appear when implementing a noise-cancelling algorithm on such a portable device, and issues of power consumption and silicon area become prominent. This paper discusses an adaptive noise cancelling scheme and presents a VLSI implementation strategy. It then considers some of the benefits of the implementation and its potential area for improvement

Maximum Likelihood Pitch Estimation Using Sinusoidal Modeling

The aim of the work presented in this thesis is to automatically extract the fundamental frequency of a periodic signal from noisy observations, a task commonly referred to as pitch estimation. An algorithm for optimal pitch estimation using a maximum likelihood formulation is presented. The speech waveform is modeled using sinusoidal basis functions that are harmonically tied together to explicitly capture the periodic structure of voiced speech. The problem of pitch estimation is casted as a model selection problem and the Akaike Information Criterion is used to estimate the pitch. The algorithm is compared with several existing pitch detection algorithms (PDAs) on a reference pitch database. The results indicate the superior performance of the algorithm in comparison with most of the PDAs. The application of parametric modeling in single channel speech segregation and the use of mel-frequency cepstral coefficients for sequential grouping are analyzed in the speech separation challenge database

Audiovisual processing for sports-video summarisation technology

In this thesis a novel audiovisual feature-based scheme is proposed for the automatic summarization of sports-video content The scope of operability of the scheme is designed to encompass the wide variety o f sports genres that come under the description ‘field-sports’. Given the assumption that, in terms of conveying the narrative of a field-sports-video, score-update events constitute the most significant moments, it is proposed that their detection should thus yield a favourable summarisation solution. To this end, a generic methodology is proposed for the automatic identification of score-update events in field-sports-video content. The scheme is based on the development of robust extractors for a set of critical features, which are shown to reliably indicate their locations. The evidence gathered by the feature extractors is combined and analysed using a Support Vector Machine (SVM), which performs the event detection process. An SVM is chosen on the basis that its underlying technology represents an implementation of the latest generation of machine learning algorithms, based on the recent advances in statistical learning. Effectively, an SVM offers a solution to optimising the classification performance of a decision hypothesis, inferred from a given set of training data. Via a learning phase that utilizes a 90-hour field-sports-video trainmg-corpus, the SVM infers a score-update event model by observing patterns in the extracted feature evidence. Using a similar but distinct 90-hour evaluation corpus, the effectiveness of this model is then tested genencally across multiple genres of fieldsports- video including soccer, rugby, field hockey, hurling, and Gaelic football. The results suggest that in terms o f the summarization task, both high event retrieval and content rejection statistics are achievable

New time-frequency domain pitch estimation methods for speed signals under low levels of SNR

The major objective of this research is to develop novel pitch estimation methods capable of handling speech signals in practical situations where only noise-corrupted speech observations are available. With this objective in mind, the estimation task is carried out in two different approaches. In the first approach, the noisy speech observations are directly employed to develop two new time-frequency domain pitch estimation methods. These methods are based on extracting a pitch-harmonic and finding the corresponding harmonic number required for pitch estimation. Considering that voiced speech is the output of a vocal tract system driven by a sequence of pulses separated by the pitch period, in the second approach, instead of using the noisy speech directly for pitch estimation, an excitation-like signal (ELS) is first generated from the noisy speech or its noise- reduced version. In the first approach, at first, a harmonic cosine autocorrelation (HCAC) model of clean speech in terms of its pitch-harmonics is introduced. In order to extract a pitch-harmonic, we propose an optimization technique based on least-squares fitting of the autocorrelation function (ACF) of the noisy speech to the HCAC model. By exploiting the extracted pitch-harmonic along with the fast Fourier transform (FFT) based power spectrum of noisy speech, we then deduce a harmonic measure and a harmonic-to-noise-power ratio (HNPR) to determine the desired harmonic number of the extracted pitch-harmonic. In the proposed optimization, an initial estimate of the pitch-harmonic is obtained from the maximum peak of the smoothed FFT power spectrum. In addition to the HCAC model, where the cross-product terms of different harmonics are neglected, we derive a compact yet accurate harmonic sinusoidal autocorrelation (HSAC) model for clean speech signal. The new HSAC model is then used in the least-squares model-fitting optimization technique to extract a pitch-harmonic. In the second approach, first, we develop a pitch estimation method by using an excitation-like signal (ELS) generated from the noisy speech. To this end, a technique is based on the principle of homomorphic deconvolution is proposed for extracting the vocal-tract system (VTS) parameters from the noisy speech, which are utilized to perform an inverse-filtering of the noisy speech to produce a residual signal (RS). In order to reduce the effect of noise on the RS, a noise-compensation scheme is introduced in the autocorrelation domain. The noise-compensated ACF of the RS is then employed to generate a squared Hilbert envelope (SHE) as the ELS of the voiced speech. With a view to further overcome the adverse effect of noise on the ELS, a new symmetric normalized magnitude difference function of the ELS is proposed for eventual pitch estimation. Cepstrum has been widely used in speech signal processing but has limited capability of handling noise. One potential solution could be the introduction of a noise reduction block prior to pitch estimation based on the conventional cepstrum, a framework already available in many practical applications, such as mobile communication and hearing aids. Motivated by the advantages of the existing framework and considering the superiority of our ELS to the speech itself in providing clues for pitch information, we develop a cepstrum-based pitch estimation method by using the ELS obtained from the noise-reduced speech. For this purpose, we propose a noise subtraction scheme in frequency domain, which takes into account the possible cross-correlation between speech and noise and has advantages of noise being updated with time and adjusted at each frame. The enhanced speech thus obtained is utilized to extract the vocal-tract system (VTS) parameters via the homomorphic deconvolution technique. A residual signal (RS) is then produced by inverse-filtering the enhanced speech with the extracted VTS parameters. It is found that, unlike the previous ELS-based method, the squared Hilbert envelope (SHE) computed from the RS of the enhanced speech without noise compensation, is sufficient to represent an ELS. Finally, in order to tackle the undesirable effect of noise of the ELS at a very low SNR and overcome the limitation of the conventional cepstrum in handling different types of noises, a time-frequency domain pseudo cepstrum of the ELS of the enhanced speech, incorporating information of both magnitude and phase spectra of the ELS, is proposed for pitch estimation. (Abstract shortened by UMI.

A Probabilistic Approach to AMDF Pitch Detection

We present a probabilistic error correction technique to be used with an average magnitude difference function (AMDF) based pitch detector. This error correction routine provides a very simple method to correct errors in pitch period estimation. Used in conjunction with the computationally efficient AMDF, the result is a fast and accurate pitch detector. In performance tests on the CSTR (Center for Speech Technology Research) database, probabilistic error correction reduced the gross error rate from 6.07% to 3.29%