Topic: mfcc Goto Github
Some thing interesting about mfcc
Some thing interesting about mfcc
mfcc,Speaker Recognition System using MFCC and GMM.
User: abhay0899193
mfcc,A C++ Library for Audio Analysis
User: adamstark
Home Page: http://www.adamstark.co.uk/project/gist/
mfcc,Use machine learning models to detect lies based solely on acoustic speech information
User: alicex2020
mfcc,Deep learning using CNN for Mandarin Chinese tone classification
User: alicex2020
mfcc,Detecting emotions using MFCC features of human speech using Deep Learning
User: amanbasu
mfcc,.NET DSP library with a lot of audio processing functions
User: ar1st0crat
mfcc,a library for audio and music analysis
Organization: aubio
Home Page: https://aubio.org
mfcc,aubio plugins for Vamp
Organization: aubio
Home Page: https://aubio.org/vamp-aubio-plugins
mfcc,Kaldi-compatible online & offline feature extraction with PyTorch, supporting CUDA, batch processing, chunk processing, and autograd - Provide C++ & Python API
User: csukuangfj
Home Page: https://csukuangfj.github.io/kaldifeat
mfcc,:fire: ASR教程: https://dataxujing.github.io/ASR-paper/
User: dataxujing
mfcc,Machine learning, in numpy
User: ddbourgin
Home Page: https://numpy-ml.readthedocs.io/
mfcc,Speaker Recognition using Neural Network & Linear Regression
User: dydtjr1128
mfcc,stm32-speech-recognition-and-traduction is a project developed for the Advances in Operating Systems exam at the University of Milan (academic year 2020-2021). It implements a speech recognition and speech-to-text translation system using a pre-trained machine learning model running on the stm32f407vg microcontroller.
User: federicapaoli1
Home Page: https://github.com/FedericaPaoli1/stm32-speech-recognition-and-traduction
mfcc,SpeakerVoiceIdentifier can recognize the voice of a speaker by learning.
User: fragjage
mfcc,A program for automatic speaker identification using deep learning techniques.
User: gauravwaghmare
mfcc,Lyrics-to-audio-alignement system. Based on Machine Learning Algorithms: Hidden Markov Models with Viterbi forced alignment. The alignment is explicitly aware of durations of musical notes. The phonetic model are classified with MLP Deep Neural Network.
User: georgid
Home Page: http://mtg.upf.edu/node/3751
mfcc,Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].
User: gionanide
mfcc,Basics of Musical Instruments Classification using Machine Learning
User: guitarsai
mfcc,A RESTFUL API implementation of an authentification system using voice fingerprint
User: ihabbendidi
mfcc,Voice Alignment and Conversion with Neural Networks and the WORLD codec.
User: javierantoran
mfcc,Audio feature extraction and classification
User: jsingh811
mfcc,The human speaks a language with an accent. A particular accent necessarily reflects a person's linguistic background. The model defines accent based audio record. The result of the model could be used to determine accents and help decrease accents to English learning students and improve accents by training.
User: k-farruh
mfcc,Deep Learning model for lexical stress detection in spoken English
Organization: lexicalstressdetection
mfcc,A library for audio and music analysis, feature extraction.
Organization: libaudioflux
Home Page: https://audioflux.top
mfcc,In this work we propose two postprocessing approaches applying convolutional neural networks (CNNs) either in the time domain or the cepstral domain to enhance the coded speech without any modification of the codecs. The time domain approach follows an end-to-end fashion, while the cepstral domain approach uses analysis-synthesis with cepstral domain features. The proposed postprocessors in both domains are evaluated for various narrowband and wideband speech codecs in a wide range of conditions. The proposed postprocessor improves speech quality (PESQ) by up to 0.25 MOS-LQO points for G.711, 0.30 points for G.726, 0.82 points for G.722, and 0.26 points for adaptive multirate wideband codec (AMR-WB). In a subjective CCR listening test, the proposed postprocessor on G.711-coded speech exceeds the speech quality of an ITU-T-standardized postfilter by 0.36 CMOS points, and obtains a clear preference of 1.77 CMOS points compared to G.711, even en par with uncoded speech.
Organization: linksense
Home Page: https://ansleliu.github.io/CNN.html
mfcc,Personal wake word detector
User: mathquis
mfcc,Spectra extraction tutorials based on torch and torchaudio.
User: mechanicalsea
mfcc,A simple audio feature extraction library
Organization: mycroftai
mfcc,Implement a GRU/LSTM model using Keras, and train it to classify the languages using MFCC features
User: nipunmanral
mfcc,Live Audio MFCC Visualization in the browser using Web Audio API - https://pulakk.github.io/Live-Audio-MFCC/tutorial
User: pulakk
mfcc,[wip]Speech recognition tool-box written by Nim. Based on Arraymancer.
User: ringabout
mfcc,Python implementation of papers on emergency vehicle detection using audio signals
User: sheelabhadra
mfcc,A suite of speech signal processing tools
Organization: sp-nitech
Home Page: http://sp-tk.sourceforge.net
mfcc,:sound: spafe: Simplified Python Audio Features Extraction
User: superkogito
Home Page: https://superkogito.github.io/spafe/
mfcc,:sound: :boy: :girl:Voice based gender recognition using Mel-frequency cepstrum coefficients (MFCC) and Gaussian mixture models (GMM)
User: superkogito
mfcc,:sound: :boy: :girl: :woman: :man: Speaker identification using voice MFCCs and GMM
User: superkogito
mfcc,A implementation of Power Normalized Cepstral Coefficients: PNCC
User: supikiti
Home Page: https://www.eurasip.org/Proceedings/Eusipco/Eusipco2015/papers/1570104069.pdf
mfcc,Identify the emotion of multiple speakers in an Audio Segment
User: suyashmore
mfcc,Synchronize your subtitles using machine learning
User: tympanix
mfcc,Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras
User: x4nth055
mfcc,Constant-Q harmonic coefficients (CQHCs), a timbre feature designed for music signals.
User: zafarrafii
mfcc,Zafar's Audio Functions in Matlab for audio signal analysis: STFT, inverse STFT, mel filterbank, mel spectrogram, MFCC, CQT kernel, CQT spectrogram, CQT chromagram, DCT, DST, MDCT, inverse MDCT.
User: zafarrafii
Home Page: http://zafarrafii.com/
mfcc,Zafar's Audio Functions in Python for audio signal analysis: STFT, inverse STFT, mel filterbank, mel spectrogram, MFCC, CQT kernel, CQT spectrogram, CQT chromagram, DCT, DST, MDCT, inverse MDCT.
User: zafarrafii
Home Page: http://zafarrafii.com/
mfcc,基于DTW与MFCC特征进行数字0-9的语音识别,DTW,MFCC,语音识别,中英数据,端点检测,Digital Voice Recognition。
User: zhengyima
mfcc,python codes to extract MFCC and FBANK speech features for Kaldi
User: zitengwang
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