Analysis on performances of the optimization algorithms in CNN speech noise attenuator

In this paper, we studied the effect of the optimization algorithm of weight coefficients on the performance of the CNN(Convolutional Neural Network) noise attenuator. This system improves the performance of the noise attenuation by a deep learning algorithm using the neural network adaptive predictive filter instead of using the existing adaptive filter. Speech is estimated from a single input speech signal containing noise using 64-neuron, 16-filter CNN filters and an error back propagation algorithm. This is to use the quasi-periodic nature of the voiced sound section of the voice signal. In this study, to verify the performance of the noise attenuator for the optimization, a test program using the Keras library was written and training was performed. As a result of simulation, this system showed the smallest MSE value when using the Adam algorithm among the Adam, RMSprop, and Adagrad optimization algorithms, and the largest MSE value in the Adagrad algorithm. This is because the Adam algorithm requires a lot of computation but it has an exellent ability to estimate the optimal value by using the advantages of RMSprop and Momentum SGD

An Experimental Study on Speech Enhancement Based on a Combination of Wavelets and Deep Learning

The purpose of speech enhancement is to improve the quality of speech signals degraded by noise, reverberation, or other artifacts that can affect the intelligibility, automatic recognition, or other attributes involved in speech technologies and telecommunications, among others. In such applications, it is essential to provide methods to enhance the signals to allow the understanding of the messages or adequate processing of the speech. For this purpose, during the past few decades, several techniques have been proposed and implemented for the abundance of possible conditions and applications. Recently, those methods based on deep learning seem to outperform previous proposals even on real-time processing. Among the new explorations found in the literature, the hybrid approaches have been presented as a possibility to extend the capacity of individual methods, and therefore increase their capacity for the applications. In this paper, we evaluate a hybrid approach that combines both deep learning and wavelet transformation. The extensive experimentation performed to select the proper wavelets and the training of neural networks allowed us to assess whether the hybrid approach is of benefit or not for the speech enhancement task under several types and levels of noise, providing relevant information for future implementations.UCR::Vicerrectoría de Docencia::Ingeniería::Facultad de Ingeniería::Escuela de Ingeniería Eléctric

Recurrent Neural Networks For Accurate RSSI Indoor Localization

This paper proposes recurrent neuron networks (RNNs) for a fingerprinting indoor localization using WiFi. Instead of locating user's position one at a time as in the cases of conventional algorithms, our RNN solution aims at trajectory positioning and takes into account the relation among the received signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a weighted average filter is proposed for both input RSSI data and sequential output locations to enhance the accuracy among the temporal fluctuations of RSSI. The results using different types of RNN including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are presented. On-site experiments demonstrate that the proposed structure achieves an average localization error of $0.75$ m with $80\%$ of the errors under $1$ m, which outperforms the conventional KNN algorithms and probabilistic algorithms by approximately $30\%$ under the same test environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization, recurrent neuron network (RNN), long shortterm memory (LSTM), fingerprint-based localizatio

Signal processing algorithms for digital hearing aids

Hearing loss is a problem that severely affects the speech communication and disqualify most hearing-impaired people from holding a normal life. Although the vast majority of hearing loss cases could be corrected by using hearing aids, however, only a scarce of hearing-impaired people who could be benefited from hearing aids purchase one. This irregular use of hearing aids arises from the existence of a problem that, to date, has not been solved effectively and comfortably: the automatic adaptation of the hearing aid to the changing acoustic environment that surrounds its user. There are two approaches aiming to comply with it. On the one hand, the "manual" approach, in which the user has to identify the acoustic situation and choose the adequate amplification program has been found to be very uncomfortable. The second approach requires to include an automatic program selection within the hearing aid. This latter approach is deemed very useful by most hearing aid users, even if its performance is not completely perfect. Although the necessity of the aforementioned sound classification system seems to be clear, its implementation is a very difficult matter. The development of an automatic sound classification system in a digital hearing aid is a challenging goal because of the inherent limitations of the Digital Signal Processor (DSP) the hearing aid is based on. The underlying reason is that most digital hearing aids have very strong constraints in terms of computational capacity, memory and battery, which seriously limit the implementation of advanced algorithms in them. With this in mind, this thesis focuses on the design and implementation of a prototype for a digital hearing aid able to automatically classify the acoustic environments hearing aid users daily face on and select the amplification program that is best adapted to such environment aiming at enhancing the speech intelligibility perceived by the user. The most important contribution of this thesis is the implementation of a prototype for a digital hearing aid that automatically classifies the acoustic environment surrounding its user and selects the most appropriate amplification program for such environment, aiming at enhancing the sound quality perceived by the user. The battery life of this hearing aid is 140 hours, which has been found to be very similar to that of hearing aids in the market, and what is of key importance, there is still about 30% of the DSP resources available for implementing other algorithms

A Comparative Analysis of Machine Learning Models for Banking News Extraction by Multiclass Classification With Imbalanced Datasets of Financial News: Challenges and Solutions

Online portals provide an enormous amount of news articles every day. Over the years, numerous studies have concluded that news events have a significant impact on forecasting and interpreting the movement of stock prices. The creation of a framework for storing news-articles and collecting information for specific domains is an important and untested problem for the Indian stock market. When online news portals produce financial news articles about many subjects simultaneously, finding news articles that are important to the specific domain is nontrivial. A critical component of the aforementioned system should, therefore, include one module for extracting and storing news articles, and another module for classifying these text documents into a specific domain(s). In the current study, we have performed extensive experiments to classify the financial news articles into the predefined four classes Banking, Non-Banking, Governmental, and Global. The idea of multi-class classification was to extract the Banking news and its most correlated news articles from the pool of financial news articles scraped from various web news portals. The news articles divided into the mentioned classes were imbalanced. Imbalance data is a big difficulty with most classifier learning algorithms. However, as recent works suggest, class imbalances are not in themselves a problem, and degradation in performance is often correlated with certain variables relevant to data distribution, such as the existence in noisy and ambiguous instances in the adjacent class boundaries. A variety of solutions to addressing data imbalances have been proposed recently, over-sampling, down-sampling, and ensemble approach. We have presented the various challenges that occur with data imbalances in multiclass classification and solutions in dealing with these challenges. The paper has also shown a comparison of the performances of various machine learning models with imbalanced data and data balances using sampling and ensemble techniques. From the result, it’s clear that the performance of Random Forest classifier with data balances using the over-sampling technique SMOTE is best in terms of precision, recall, F-1, and accuracy. From the ensemble classifiers, the Balanced Bagging classifier has shown similar results as of the Random Forest classifier with SMOTE. Random forest classifier's accuracy, however, was 100% and it was 99% with the Balanced Bagging classifier

VOICE BIOMETRICS UNDER MISMATCHED NOISE CONDITIONS

This thesis describes research into effective voice biometrics (speaker recognition) under mismatched noise conditions. Over the last two decades, this class of biometrics has been the subject of considerable research due to its various applications in such areas as telephone banking, remote access control and surveillance. One of the main challenges associated with the deployment of voice biometrics in practice is that of undesired variations in speech characteristics caused by environmental noise. Such variations can in turn lead to a mismatch between the corresponding test and reference material from the same speaker. This is found to adversely affect the performance of speaker recognition in terms of accuracy. To address the above problem, a novel approach is introduced and investigated. The proposed method is based on minimising the noise mismatch between reference speaker models and the given test utterance, and involves a new form of Test-Normalisation (T-Norm) for further enhancing matching scores under the aforementioned adverse operating conditions. Through experimental investigations, based on the two main classes of speaker recognition (i.e. verification/ open-set identification), it is shown that the proposed approach can significantly improve the performance accuracy under mismatched noise conditions. In order to further improve the recognition accuracy in severe mismatch conditions, an approach to enhancing the above stated method is proposed. This, which involves providing a closer adjustment of the reference speaker models to the noise condition in the test utterance, is shown to considerably increase the accuracy in extreme cases of noisy test data. Moreover, to tackle the computational burden associated with the use of the enhanced approach with open-set identification, an efficient algorithm for its realisation in this context is introduced and evaluated. The thesis presents a detailed description of the research undertaken, describes the experimental investigations and provides a thorough analysis of the outcomes