Topic: mfcc-features Goto Github
Some thing interesting about mfcc-features
Some thing interesting about mfcc-features
mfcc-features,GTZAN Music genre classification using Logistic regression and SVM.
User: abdoulsn
mfcc-features,Classification of urban sounds such as air conditioner, jackhammer, drilling, siren, street music, engine idling and children playing by using Mel-frequency Cepstral Coefficients (MFCCs) as audio feature and CNN algorithm.
User: abhishk12
mfcc-features,Classify music in two categories progressive rock and non-progressive rock using mfcc features, MLP, and CNN.
User: adityadutt
mfcc-features,Tiny Machine Learning Snoring Detection Model for Embedded devices
User: adrianagaler
mfcc-features,Genre Detection of Bengali Rabindranath Tagore's Song Based On Audio Data.
User: akashmodak97
mfcc-features,Voice Activity Detector based on MFCC features and DNN model
User: alexkly
mfcc-features,A machine learning model is trained to determine the word in an audio file
User: amanbudhraja
mfcc-features,Tackle accent classification and conversion using audio data, leveraging MFCCs and spectrograms. Models differentiate accents and convert audio between accents
User: arvind2903
mfcc-features,Bali has a diversity of arts that has been recognized by the world, where one of the most famous Balinese arts is the Karawitan art, especially the Kendang Tunggal instrument. Notation documentation or more commonly known as music transcription, can make learning a song easier, and in the case of this research, it makes it easier to learn to play the Kendang Tunggal instrument. The first approach method used to document a kendang tunggal song is onset detection. Onset is when the signal experiences an attack period, which helps segment the sound color of the drum instrument. The segmented kendang tunggal sound color classification uses the Backpropagation algorithm with several features of the frequency domain and time domain as a characteristic of the sound color. Then the kendang tunggal song is revived into a synthetic sound with the Mel Spectral Approximation filter. Based on the research, the optimal parameter for drum sound color segmentation with onset detection is the hop size 110 with normalization of the features on its onset detection function. The optimal backpropagation architecture obtained with a learning rate of 0.9, neurons 10, and epoch 2000 produces an accuracy of 60.85%. The synthesis method using the Mel Log Spectrum Approximation can make synthetic sounds similar to kendang songs with an accuracy of 83.33%
User: bayuwira
mfcc-features,Deep learning-based audio spoofing attack detection experiments for speaker verification.
User: bekirbakar
mfcc-features,β κ°μ μΈμ λͺ¨λΈ κ°λ° β
Organization: brave-cookie
mfcc-features,A Python implementation of STFT and MFCC audio features from scratch
User: brihijoshi
mfcc-features,Using a raspberry pi, we listen to the coffee machine and count the number of coffee consumption
Organization: datarootsio
mfcc-features,Recognition of Emotion from Speech directly
User: debapriya-tula
mfcc-features,A repos for USTH Digital Signal Processing 2020 Group 3 project. It's quite obvious in the title.
User: dinhanhx
mfcc-features,Repository for CIKM 2020 resource track paper: MAEC: A Multimodal Aligned Earnings Conference Call Dataset for Financial Risk Prediction
User: earnings-call-dataset
Home Page: https://dl.acm.org/doi/10.1145/3340531.3412879
mfcc-features,Voice Activity Detection and signal segmentation in time windows. Feature extraction in time and frequency domain. Classification in ten individual speakers.
User: exarchou
mfcc-features,This project was my final Bachelor's degree thesis. In it I decided to mix my passion, music, and the syllabus that I liked the most in my degree, deep learning.
User: fandosa
mfcc-features,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-features,Voice Activity Detection based on Deep Learning & TensorFlow
User: filippogiruzzi
mfcc-features,Implementation of Mel-Frequency Cepstral Coefficients (MFCC) extraction
User: filiptirnanic96
mfcc-features,
User: geekysethi
mfcc-features,A corpus that can be used to train English-to-Italian End-to-End Speech-to-Text Machine Translation models
User: giuseppe-della-corte
mfcc-features,Multi-class audio classification with MFCC features using CNN
User: harmanpreet93
mfcc-features,Application of a convolutional neural network (CNN) to accurately classify urban sounds in a bid to increase pedestrian safety using the UrbanSound8k dataset.
User: hassanmahmoodkhan
mfcc-features,A RESTFUL API implementation of an authentification system using voice fingerprint
User: ihabbendidi
mfcc-features,In this challenge, the goal is to learn to recognize which of several English words is pronounced in an audio recording. This is a multiclass classification task.
User: jason-oleana
mfcc-features,MFCC features + SVM for speech emotion classification
User: jason-oleana
mfcc-features,Fluency level classifier of L2 English speech
User: jjordanoc
mfcc-features,Audio feature extraction and classification
User: jsingh811
mfcc-features,XSpeech: A Novel Deep Learning Approach to Classifying Stutters
User: justanotherinternetguy
mfcc-features,RespireNet is an innovative web-based application that harnesses the capabilities of deep learning and Mel-frequency cepstral coefficients (MFCC) as a feature extraction technique for accurate respiratory disease prediction. The primary objective of this user-friendly web application is to facilitate early detection.
User: k-gokulappadurai
Home Page: https://respiratory-disease-prediction.onrender.com/
mfcc-features,Common-lisp implementation of MFCC
User: kar7hik
mfcc-features,Development of a Voice Activity Detector and a Speaker Recognition System. Feature extraction in time and frequency domain. Classification in ten individual speakers.
User: konstantd
mfcc-features,Library to extract MFCC features from audio signal
Organization: linto-ai
mfcc-features,Classify and recognize emotions through voice signal in a foreign language
User: luisa13
mfcc-features,Hate speech detection in audio for English and Kiswahili languages
User: lusanji
mfcc-features,Java Implementation of the Sonopy Audio Feature Extraction Library by MycroftAI
User: mikex86
mfcc-features,Another project for classifying Covid and non-covid patients through cough sound. Using CRNN-Attention model with the sound data converted into image data
User: mrzaizai2k
mfcc-features,Implementation of Persian Isolated-Digits Recognition with Matlab
User: parham1998
mfcc-features,recognizing spoken Bangla numbers using MFCCs and CNN.
User: piasroy
mfcc-features,Music Genre Classification using MFCC + ANN
User: reshalfahsi
mfcc-features,π This repository contains basic audio π processing code with feature extraction explained. πΆ πΆ πΆ
User: ribin-baby
mfcc-features,Signal Processing Course project
User: romanyshyn-natalia
mfcc-features,Penerapan metode Random Forest dalam klasifikasi Genre Musik menggunakan ekstraksi fitur MFCC.
User: sandiindika
mfcc-features,Audio classification using a simple SVM classifier making use of MFCC and Spectrogram features coded from scratch
User: sarthak268
mfcc-features,Detect alcohol induced intoxication level from a voice sample
User: shreeshan
mfcc-features,Machine Learning and Deep learning techniques to Classify Music Genre
User: sk-singla
mfcc-features,Audio command recognition by DTW and classification
User: thangdnsf
mfcc-features,An automatic speaker recognition system built from digital signal processing tools, Vector Quantization and LBG algorithm
User: tharunchitipolu
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