نتایج جستجو برای: mel frequency cepstral coefficient

تعداد نتایج: 644186  

Journal: :Informatica (Slovenia) 2008
Mohamed Cherif Amara Korba Djemil Messadeg Rafik Djemili Hocine Bourouba

To improve the performance of Automatic Speech Recognition (ASR) Systems, a new method is proposed to extract features capable of operating at a very low signal-to-noise ratio (SNR). The basic idea introduced in this article is to enhance speech quality as the first stage for Mel-cepstra based recognition systems, since it is well-known that cepstral coefficients provided better performance in ...

2012
Priyanka Mishra Suyash Agrawal

Human Voice is characteristic for an individual. The ability to recognize the speaker by his/her voice can be a valuable biometric tool with enormous commercial as well as academic potential. Commercially, it can be utilized for ensuring secure access to any system. Academically, it can shed light on the speech processing abilities of the brain as well as speech mechanism. In fact, this feature...

Journal: :CoRR 2010
Lindasalwa Muda Mumtaj Begam I. Elamvazuthi

Digital processing of speech signal and voice recognition algorithm is very important for fast and accurate automatic voice recognition technology. The voice is a signal of infinite information. A direct analysis and synthesizing the complex voice signal is due to too much information contained in the signal. Therefore the digital signal processes such as Feature Extraction and Feature Matching...

2013
Tomyslav Sledevič Artūras Serackis Gintautas Tamulevičius Dalius Navakauskas

Paper presents an comparative evaluation of features extraction algorithm for a real-time isolated word recognition system based on FPGA. The Mel-frequency cepstral, linear frequency cepstral, linear predictive and their cepstral coefficients were implemented in hardware/software design. The proposed system was investigated in speaker dependent mode for 100 different Lithuanian words. The robus...

2017
Daulappa Guranna BHALKE Betsy RAJESH Dattatraya Shankar BORMANE

This paper presents the Automatic Genre Classification of Indian Tamil Music andWestern Music using Timbral and Fractional Fourier Transform (FrFT) based Mel Frequency Cepstral Coefficient (MFCC) features. The classifier model for the proposed system has been built using K-NN (K-Nearest Neighbours) and Support Vector Machine (SVM). In this work, the performance of various features extracted fro...

1999
Rathinavelu Chengalvarayan

In this paper, a new approach for linear prediction (LP) analysis is explored, where predictor can be computed from a mel-warped subband-based autocorrelation functions obtained from the power spectrum. For spectral representation a set of multi-resolution cepstral features are proposed. The general idea is to divide up the full frequency-band into several subbands, perform the IDFT on the mel ...

1994
Keiichi Tokuda Hidetoshi Matsumura Takao Kobayashi Satoshi Imai

In this paper, we propose an ADPCM coder which uses a backward adaptive predictor based on the adap tive mel-cepstral analysis. The spectrum represented by the mel-cepstral coefficients has frequency resolution similar to that of the human ear which has high resolution at low frequencies. In the coder, since the transfer functions of noise shaping and postflltering are also defined through the ...

1998
Keiichi Tokuda Takashi Masuko Jun Hiroi Takao Kobayashi Tadashi Kitamura

This paper presents a very low bit rate speech coder based on HMM (Hidden Markov Model). The encoder carries out phoneme recognition, and transmits phoneme indexes, state durations and pitch information to the decoder. In the decoder, phoneme HMMs are concatenated according to the phoneme indexes, and a sequence of mel-cepstral coefficient vectors is generated from the concatenated HMM by using...

2012
Mangesh S. Deshpande Raghunath S. Holambe

Mel Frequency Cepstral Coefficient (MFCC) features are widely used as acoustic features for speech recognition as well as speaker recognition. In MFCC feature representation, the Mel frequency scale is used to get a high resolution in low frequency region, and a low resolution in high frequency region. This kind of processing is good for obtaining stable phonetic information, but not suitable f...

Journal: :International Journal of Advanced Computer Science and Applications 2023

Speaker’s audio is one of the unique identities speaker. Nowadays not only humans but machines can also identify by their audio. Machines different properties human voice and classify speaker from speaker’s Speaker recognition still challenging with degraded limited dataset. be identified effectively when feature extraction more accurate. Mel-Frequency Cepstral Coefficient (MFCC) mostly used me...

نمودار تعداد نتایج جستجو در هر سال

با کلیک روی نمودار نتایج را به سال انتشار فیلتر کنید