Speaker Identification System for Hindi And Marathi Languages using Wavelet and Support Vector Machine

In this paper, a speaker identification system using speech processing for Hindi and Marathi languages is developed. Database of common words between Hindi and Marathi languages whose script is common but pronunciation is different is created. Here feature extraction is performed by using Wavelet Packet Decomposition (WPD) and classification is performed by using Support Vector Machine (SVM). As compared to the conventional feature extraction techniques wavelet transform is very much suitable for processing speech signals which are non-stationary in nature because of its efficient time frequency localizations and multi-resolution characteristics. Also SVM is well suitable for addressing speaker identification task. Recognition accuracy of 99.77% is obtained whereas real time recognition accuracy of 84.66% is obtained in identical condition using this hybrid architecture of WPD and SVM. In noisy conditions recognition accuracy of 60% is obtained. DOI: 10.17762/ijritcc2321-8169.16049

Reconhecimento de Dígitos de Medidores de Energia por meio da Voz no Contexto de um Aplicativo de Autoleitura / Digit Recognition of Energy Meters through Voice in the Context of an Authentication Application

A Agência Nacional de Energia Elétrica (ANEEL) destaca que perdas não-técnicas estão relacionadas a entraves no processo de leitura de consumo. Para a redução dessas falhas, uma alternativa factível e de menor custo seria a leitura realizada pelo próprio consumidor, denominada de autoleitura.  Este processo leva em consideração o uso de plataformas digitais, através das quais o consumidor registraria e enviaria as informações de consumo.  Uma etapa importante desse processo é o reconhecimento automático de dígitos de medidores por meio da voz.  Este trabalho, portanto, propõe um método para a realização dessa tarefa, que utiliza processamento de áudio e inteligência computacional. Para a extração de características de áudio, utiliza-se Mel-frequency Cepstral Coefficients (MFCC) e MelSpectrogram de forma combinada. O método apresenta Recall de 94,74%; Precision de 94,91%; F1 score de 94,72% e 0,9419 de índice Kappa utilizando-se o classificador Support Vector Machine (SVM)

Time series classification with representation ensembles

Time series has attracted much attention in recent years, with thousands of methods for diverse tasks such as classification, clustering, prediction, and anomaly detection. Among all these tasks, classification is likely the most prominent task, accounting for most of the applications and attention from the research community. However, in spite of the huge number of methods available, there is a significant body of empirical evidence indicating that the 1-nearest neighbor algorithm (1-NN) in the time domain is “extremely difficult to beat”. In this paper, we evaluate the use of different data representations in time series classification. Our work is motivated by methods used in related areas such as signal processing and music retrieval. In these areas, a change of representation frequently reveals features that are not apparent in the original data representation. Our approach consists of using different representations such as frequency, wavelets, and autocorrelation to transform the time series into alternative decision spaces. A classifier is then used to provide a classification for each test time series in the alternative domain. We investigate how features provided in different domains can help in time series classification. We also experiment with different ensembles to investigate if the data representations are a good source of diversity for time series classification. Our extensive experimental evaluation approaches the issue of combining sets of representations and ensemble strategies, resulting in over 300 ensemble configurations.São Paulo Research Foundation (FAPESP) (grant #2012/08923-8, #2013/26151-5, and #2015/07628-0)CNPq (grant #446330/2014-0 and #303083/2013-1)International Symposium on Advances in Intelligent Data Analysis - IDA (14. 2015 Saint Etienne