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1d-speech-emotion-recognition

Speech Emotion Recognition from raw speech signals using 1D CNN-LSTM

2017-interspeech-challenge

2019-spring-dl

CMP SCI 4390/5390 - Deep Learning

2nd-freesound-audio-tagging-2019

3d-convolutional-speaker-recognition

:speaker: Deep Learning & 3D Convolutional Neural Networks for Speaker Verification

a-project-on-omg-emotion-challenge-2018

This is the code repository for the OMG emotion challenge 2018 from team HKUST-NISL2018.

abaw-siu

acoustic-feature-fusion_chime18

Code for the paper titled "Acoustic Features Fusion using Attentive Multi-channel Deep Architecture" paper using Keras and tensorflow

acoustic-scene-classification-using-a-deeper-training-method-for-convolution-neural-networks

acoustic_scene_dcase2022

Codes related to acoustic scene classification task for DCASE 2022

actrectut

MATLAB Human Activity Recognition Toolbox

ad_detection_open

Detecting AD from Audio Data Using Neural Networks with Bottleneck Features and Data Augmentation

adae_dnn_audio_tagging

addetection

ICASSP 2023 SPGC Challenge: Multilingual Alzheimer's Dementia Recognition through Spontaneous Speech

adresschallenge

adviser

ADvISER is a flexible framework to encourage task-oriented dialog system research & development

ae_ts

Auto encoder for time series

aemotion

Audio emotion recognition with convnets

aesrc2020

a deep accent recognition network

aff-wild-models

affect-recognition

Parses the WESAD dataset and builds/inspects models for affect recognition

ageestimationframework

Effective training of convolutional neural networks for age estimation based on knowledge distillation

ai_clinician

Reinforcement learning for medical decisions

albumentations

fast image augmentation library and easy to use wrapper around other libraries

allennlp

An open-source NLP research library, built on PyTorch.

alzheimers-dementia

Alzheimer's dementia classification and MMSE score regression

amazing-feature-engineering

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

ami-cough-annotations

Annotations of cough events in the AMI corpus audio recordings

an-end-to-end-multitask-model-to-improve-speech-emotion-recognition

code for the ICASSP conference paper

an-end-to-end-multitask-model-to-improve-speech-emotion-recognition-1

code for the ICASSP conference paper