Implementation of Mel Frequency Cepstral Coefficient and Dynamic Time Warping For Bird Sound Classification
نویسندگان
چکیده
منابع مشابه
Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques
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...
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We present a novel MFCC-based scheme for the Bandwidth Extension (BWE) of narrowband speech. BWE is based on the assumption that narrowband speech (0.3–3.4 kHz) correlates closely with the highband signal (3.4–7 kHz), enabling estimation of the highband frequency content given the narrow band. While BWE schemes have traditionally used LP-based parametrizations, our recent work has shown that MF...
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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...
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Most speech recognition systems are based on melfrequency cepstral coefficients and their firstand secondorder derivatives. The derivatives are normally approximated by fitting a linear regression line to a fixed-length segment of consecutive frames. The time resolution and smoothness of the estimated derivative depends on the length of the segment. We present an approach to improve the represe...
متن کاملAlternative Frequency Scale Cepstral Coefficient for Robust Sound Event Recognition
There are two issues when applying MFCC for sound event recognition: 1) sound events have a broader spectral range than speech thus the log-frequency scale is less informative; 2) low frequency noise is more prevalent thus the log-frequency scale captures more noise. To address these issues, we study two alternative frequency scales and show that they outperform MFCCs for sound event recognitio...
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ژورنال
عنوان ژورنال: Conference SENATIK STT Adisutjipto Yogyakarta
سال: 2019
ISSN: 2528-1666,2337-3881
DOI: 10.28989/senatik.v5i0.326