Amaro A. de Lima

A rule-based grapheme-phone converter and stress determination for Brazilian Portuguese natural language processing

This paper presents a grapheme-phone converter and stress determination algorithm based on rules. The proposed set of rules was implemented and tested on a randomly chosen extract of the CETEN-Folha text database. Computer experiments show it is achieve an accuracy of 97.44% and 98.58%, respectively, for the grapheme-phone converter and the stress determination algorithm.

A blind algorithm for reverberation-time estimation using subband decomposition of speech signals

An algorithm for blind estimation of reverberation time (RT) in speech signals is proposed. Analysis is restricted to the free-decaying regions of the signal, where the reverberation effect dominates, yielding a more accurate RT estimate at a reduced computational cost. A spectral decomposition is performed on the reverberant signal and partial RT estimates are determined in all signal subbands, providing more data to the statistical-analysis stage of the algorithm, which yields the final RT estimate. Algorithm performance is assessed using two distinct speech databases, achieving 91% and 97% correlation with the RTs measured by a standard nonblind method, indicating that the proposed method blindly estimates the RT in a reliable and consistent manner.

On the quality-assessment of reverberated speech

This paper addresses the problem of quantifying the reverberation effect in speech signals. The perception of reverberation is assessed based on a new measure combining the characteristics of reverberation time, room spectral variance, and direct-to-reverberant energy ratio, which are estimated from the associated room impulse response (RIR). The practical aspects behind a robust RIR estimation are underlined, allowing an effective feature extraction for reverberation evaluation. The resulting objective metric achieves a correlation factor of about 90% with the subjective scores of two distinct speech databases, illustrating the systems ability to assess the reverberation effect in a reliable manner.

Feature analysis for quality assessment of reverberated speech

This paper analyzes the ability of several measurements to quantify the reverberation effect in speech signals. We consider an intrusive scheme, in which the clean and reverberated signals are available, allowing one to estimate the corresponding room impulse response (RIR) signal. An artificial neural network (ANN) is trained for all features and used in a regression approach to estimate the human perceptual evaluation in a mean opinion score (MOS) 1–5 scale. Dimensionality reduction approaches are applied to generate a simpler ANN regression, establishing the most representative features for the problem at hand. A correlation level of 85% with subjective test scores was achieved by reducing the input-vector dimension from 10 to 3, including only the features of reverberation time, room spectral variance, and direct-to-reverberant energy ratio.

The influence of feature vector on the classification of mechanical faults using neural networks

This paper investigates the problem of automatic detection of rotating-machine faults based on vibration signals acquired during machine operation. In particular, two new signal features, namely the kurtosis and entropy, are considered along with main spectral peaks to discriminate between several machine conditions: normal operation, (vertical and horizontal) misalignment, unbalanced load and bearing faults. Moreover, the inclusion of one set of three accelerometers for each roller bearing associated to the system acquiring more vibration signals also affects the generation of feature vector and is part of our proposal. In order to evaluate the rotating machine fault classification, a database of 1951 fault scenarios with several different fault intensities and rotating frequencies was designed and recorded, taking into consideration the specificities of the proposed machine learning task. The artificial neural networks recognition system employed in this work reached 95.8% of overall accuracy, showing the efficiency of the proposed approach.

Blind estimators for reverberation time and direct-to-reverberant energy ratio using subband speech decomposition

This paper describes algorithms for estimating two important features associated with the reverberation effect on speech signals: the reverberation time and direct-to-reverberant energy ratio. Both methods are referred to as blind algorithms in the sense that they are entirely based on the reverberant signal itself, not depending on the knowledge of the clean original signal. Proposed schemes use subband analysis to generate more and more reliable information, which is post-processed using basic statistical analysis to provide the desired estimate for each particular feature. Modifications on the original estimation algorithms are introduced to cope with lower SNRs. Performance of both algorithms is assessed under the ACE Challenge scope, which included a set of 288 speech signals for training and 4500 signals for final test. Results indicate the effectiveness of both techniques particularly in high-SNR situations.

Quality Evaluation of Reverberation in Audioband Speech Signals

Modern telepresence systems constitute a new challenge for quality assessment of multimedia signals. This paper focuses on the evaluation of the reverberation impairment for audioband speech signals. A review on the reverberation effect is presented, with emphasis given on the mathematical modeling of its components, including early reflections and late reverberation. A subjective test for evaluating the human perception of the reverberation phenomenon is completely described, from its conception to the final results. Analyses are provided comparing the average subjective grades to current quality-evaluation standards for speech and audio signals. It is verified how the reverberation perception can be mapped onto three main system characteristics: reverberation time (associated to the room acoustical properties), source-microphone distance, and room volume. Direct estimation of these parameters from the room impulse response is discussed. One established reverberation measure is then revisited in the audioband speech context, showing high correlation with the subjective grades previously obtained.

Audio anomaly detection on rotating machinery using image signal processing

This paper addresses the problem of anomaly detection on rotating machinery in industrial environments using single channel audio signals. The proposed algorithm is based on image processing feature analysis obtained from the image representation of the Short-time Fourier Transform of reference and degraded audio signals. In order to assess the potential of the algorithm, a 8 signals database is recorded. The proposed algorithm is able to separate signals of machinery normal behavior from signals of machinery anomalous behavior with 100% hit rate using the recorded database.

Sparse KPCA for feature extraction in speech recognition

This paper presents an analysis of the applicability of sparse kernel principal component analysis (SKPCA) for feature extraction in speech recognition, as well as a proposed approach to make the SKPCA technique realizable for a large amount of training data, which is a usual context in speech recognition systems. Although the KPCA (kernel principal component analysis) has proved to be an efficient technique for being applied to speech recognition, it has the disadvantage of requiring training data reduction, when its amount is excessively large. The standard approach to perform this data reduction is to randomly choose frames from the original data set, which does not necessarily provide a good statistical representation of the original data set. In order to solve this problem a likelihood related re-estimation procedure was applied to the KPCA framework, thus creating the SKPCA. The experimental results show the efficiency of SKPCA technique with the proposed approach over the KPCA with the standard sparse solution using randomly chosen frames and the standard feature extraction techniques.

Application of Machine Learning to Evaluate Unbalance Severity in Rotating Machines

This paper proposes two modifications in a classification method for unbalancing fault severity analysis in rotating machines based on the unbalancing mass force. The unbalancing severity was categorized into three severity levels, namely High (H), Medium (M) and Low (L). The feature vectors used information from discrete-time Fourier transform (DFT), kurtosis and entropy from the vibration signals. Similarity based Model (SBM) and Kernel discriminant analysis (KDA) techniques were applied in order to evaluate the feature discrimination and reduce the input feature space. All these techniques were tested in a random forest classifier. Test results indicate that non-linear transformations to the feature space combined to random forest can further improve the classification of unbalancing severity defect, by reducing the feature space dimension from 31 to 6.