Query by Singing/Humming (QBSH) is a Music Information Retrieval (MIR) system with small audio excerpt as query. The rising availability of digital music stipulates effective music retrieval methods. Further, MIR systems support content based searching for music and requires no musical acquaintance. Current work on QBSH focuses mainly on melody features such as pitch, rhythm, note etc., size of...

Cepstral coefficients derived either through linear prediction (LP) analysis or from filter bank are perhaps the most commonly used features in currently available speech recognition systems. In this paper, we propose spectral subband centroids as new features and use them as supplement to cepstral features for speech recognition. We show that these features have properties similar to formant f...

<span lang="EN-US">Speech recognition is one of the important applications artificial intelligence (AI). Speech aims to recognize spoken words regardless who speaking them. The process voice involves extracting meaningful features from and then classifying these into their classes. This paper presents a neural network classification system for Arabic letters. will study effect changing mu...

This project's 'HMM Based Automatic Speech Recognition Analysis main motive is just to generate an Automatic speech recognition which is clear an accurate using Hidden Markov Model (HMM) to get accurate results at number of frequency ranges related to human voice. Here is a record of 12 different words which is recorded by using a number of different speakers that includes male and female both ...

This paper investigates the importance of spectrotemporal characteristics of the source excitation signal for speaker recognition. We propose an effective feature extraction technique for obtaining essential timefrequency information from the linear prediction (LP) residual signal, which are closely related to the glottal excitation of individual speaker. With pitch synchronous analysis, wavele...

This paper presents an effort to provide a more efficient speech signal representation, which aims to be incorporated into an automatic speech recognition system. Modified cepstral coefficients, derived from a multiresolution auditory spectrum are proposed. The multiresolution spectrum was obtained using sliding single point discrete Fourier transformations. It is shown that the obtained spectr...

This study presents a compact L-points discrete cosine transform (DCT) hardware accelerator for M-points Mel-scale Frequency Cepstral Coefficients (MFCC). The main contributions of this work can be summarized as 1) proposing an algorithm with lower complexity; 2) achieving higher accuracy performance; 3) implementing low-cost unique group coefficients. For derivation, the proposed...