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Team CE.net

by roypriyanshu09 Updated: Nov 2, 2021

This kit is helpful for audio analysis. Audio information plays a rather important role in the increasing digital content that is available today; resulting in a need for methodologies that automatically analyze such content. Speaker Identification is one of the vital field of research based upon Voice Signals. Its other notable fields are: Speech Recognition, Speech-to-Text Conversion, and vice versa, etc. Mel Frequency Cepstral Coefficient (MFCC) is considered a key factor in performing Speaker Identification. But, there are other features lists available as an alternate to MFCC; like- Linear Predictor Coefficient (LPC), Spectrum Sub-band Centroid (SSC), Rhythm, Turbulence, Line Spectral Frequency (LPF), ChromaFactor, etc. Gaussian Mixture Model (GMM) is the most popular model for training on our data. The training task can also be executed on other significant models; viz. Hidden Markov Model (HMM). Recently, most of the model training phase for a speaker identification project is executed using Deep learning; especially, Artificial Neural Networks (ANN). In this project, we are mainly focused on implementing MFCC and GMM in pair to achieve our target. We have considered MFCC with “tuned parameters” as the primary feature and delta- MFCC as secondary feature. And, we have implemented GMM with some tuned parameters to train our model. We have performed this project on two different kinds of Dataset; viz. “VoxForge” Dataset and a custom dataset which we have prepared by ourselves. We have obtained an outstanding result on both of these Datasets; viz. 100% accuracy on VoxForge Dataset and 95.29 % accuracy on self prepared Dataset. We demonstrate that speaker identification task can be performed using MFCC and GMM together with outstanding accuracy in Identification/ Diarization results.

Group Name 1

Pre-processing audio

spleeterby deezer

Python star image 18873 Version:v2.3.0

License: Permissive (MIT)

Deezer source separation library including pretrained models.

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spleeterby deezer

Python star image 18873 Version:v2.3.0 License: Permissive (MIT)

Deezer source separation library including pretrained models.
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FFmpegby FFmpeg

C star image 29175 Version:n3.0

License: Others (Non-SPDX)

Mirror of https://git.ffmpeg.org/ffmpeg.git

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FFmpegby FFmpeg

C star image 29175 Version:n3.0 License: Others (Non-SPDX)

Mirror of https://git.ffmpeg.org/ffmpeg.git
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Group Name 2

Audio analysis

pyAudioAnalysisby tyiannak

Python star image 4186 Version:Current

License: Permissive (Apache-2.0)

Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications

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pyAudioAnalysisby tyiannak

Python star image 4186 Version:Current License: Permissive (Apache-2.0)

Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications
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python_speech_featuresby jameslyons

Python star image 1914 Version:0.6.1

License: Permissive (MIT)

This library provides common speech features for ASR including MFCCs and filterbank energies.

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python_speech_featuresby jameslyons

Python star image 1914 Version:0.6.1 License: Permissive (MIT)

This library provides common speech features for ASR including MFCCs and filterbank energies.
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essentiaby MTG

Jupyter Notebook star image 2082 Version:v2.1_beta5

License: Strong Copyleft (AGPL-3.0)

C++ library for audio and music analysis, description and synthesis, including Python bindings

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essentiaby MTG

Jupyter Notebook star image 2082 Version:v2.1_beta5 License: Strong Copyleft (AGPL-3.0)

C++ library for audio and music analysis, description and synthesis, including Python bindings
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librosaby librosa

Python star image 5120 Version:0.9.1

License: Permissive (ISC)

Python library for audio and music analysis

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librosaby librosa

Python star image 5120 Version:0.9.1 License: Permissive (ISC)

Python library for audio and music analysis
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Deployment Information

For the deployment of pyAudioAnalysis you can find a list of library dependencies below : a.) NUMPY b.) MATPLOTLIB c.) SCIPY d.) SKLEARN e.) Hmmlearn f.) Simplejson g.) eyeD3 h.) pydub

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