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se_relativisticgan

Keras framework for speech enhancement using relativistic GANs

se_snmf_nat

Speech enhancement (Interspeech 2016, Ideal)

selenium

A browser automation framework and ecosystem.

self-instruct

Aligning pretrained language models with instruction data generated by themselves.

self-supervised-low-res-speech

This project transfert the self supervised Wav2vec2 representation to low ressources languages

self_attention_alignment

Deep model with built-in self-attention alignment for acoustic echo cancellation, Pytorch implement

self_supervised_ahc

Contains code for Deep Self Supervised Heirarchical Clustering for Speaker Diarization

selfsimsr

Exploiting Self-Similarities for Single Frame Super-Resolution (ACCV 2010)

selfsupervised-denoising

High-Quality Self-Supervised Deep Image Denoising - Official TensorFlow implementation of the NeurIPS 2019 paper

senior-thesis

A challenging problem in current hearing aid technology is source separation of a desired acoustic signal from unwanted acoustic signal such as speech, noise and reverberations. Currently, the approach of blind-source separation, which does not utilize any information about the acoustic path between a speaker and a microphone, runs into difficulty when separating out the desired acoustic signal from the audio mixture when the room is very reverberant. To circumvent this issue, the approach that we use is to aid the microphone array system in beamforming through the use of a pilot signal or so-called ‘beacon.’ The beacon is an audible-range or ultrasonic-range acoustic pilot signal that would be placed near the desired acoustic source. It would provide the needed acoustic path information - in the form of an Acoustic Impulse Response - which would help a beamformer, along with the second-order statistics of the interference signals, to separate out the desired acoustic signal and increase its signal-to-noise ratio. Multiple lengths and frequency range pilot signals are tested to determine the most useful pilot signal in a human environment.

sentence-completion

MSR sentence completion challenge implementation (RNNLM, LSA total similarity, word2vec total similarity) in Python

sentence2vec

Tools for mapping a sentence with arbitrary length to vector space

sentence2vec-1

Sentence2vec as described in A SIMPLE BUT TOUGH TO BEAT BASELINE FOR SENTENCE EMBEDDINGS by Sanjeev Arora, Yingyu Liang, Tengyu Ma

sentence_classification

sentence classification using CNN/LSTM/GRU

sepia-docs

Documentation and Wiki for SEPIA. Please post your questions and bug-reports here in the issues section! Thank you :-)

seq2seqvc

Sequence-to-sequence Acoustic Modelling for Voice Conversion

setuptools

Official project repository for the Setuptools build system

sffhmm

Support code for the TASLP 2013 on Source/Filter Factorial HMM for Formant Tracking

sframe

SFrame: Scalable tabular and graph data-structures built for out-of-core data analysis and machine learning.

sgmse

Score-based Generative Models (Diffusion Models) for Speech Enhancement and Dereverberation

sheffield_clarity_cec1_entry

Implementation of Sheffield entry for Clarity enhancement challenge.

shenlan

shennong

A Python toolbox for speech features extraction

show-attend-and-tell

TensorFlow Implementation of "Show, Attend and Tell"

show_attend_and_tell.tensorflow

shufflenet

This is a fast caffe implementation of ShuffleNet.

shufflenet-1

ShuffleNet in PyTorch. Based on https://arxiv.org/abs/1707.01083

sid-ternary-network

Code and models to accompany "Energy Efficient SID for Low-Precision Networks"

sidekit

For further release go to: https://git-lium.univ-lemans.fr/speaker/sidekit

siggraph2016_colorization

Code for the paper 'Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification'.