soham97/awesome-sound_event_detection

Reading list for research topics in Sound AI

Size: 145 KB - Last synced at: 22 days ago - Pushed at: 10 months ago - Stars: 180 - Forks: 8

Jonathan-Greif/QBV

This repository provides the code for "Improving Query-by-Vocal Imitation with Contrastive Learning and Audio Pretraining", presented at DCASE 2024. The paper addresses the challenge of audio retrieval using vocal imitations as queries, proposing a dual encoder architecture that leverages pretrained CNNs and an adapted NT-Xent loss for fine-tuning.

Language: Python - Size: 1.61 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 1 - Forks: 0

oncescuandreea/audio_egovlp

This is the official codebase used for obtaining the results in the ICASSP 2024 paper: A SOUND APPROACH: Using Large Language Models to generate audio descriptions for egocentric text-audio retrieval

Language: Python - Size: 13.7 MB - Last synced at: 9 months ago - Pushed at: 9 months ago - Stars: 3 - Forks: 0

microsoft/WavText5K

Web-crawl for "Audio Retrieval with WavText5K and CLAP Training"

Language: Python - Size: 419 KB - Last synced at: 4 days ago - Pushed at: over 2 years ago - Stars: 49 - Forks: 0

soham97/sound_ai_progress

Tracking states of the arts and recent results (bibliography) on sound tasks.

Size: 51.8 KB - Last synced at: 10 months ago - Pushed at: over 2 years ago - Stars: 28 - Forks: 1

akoepke/audio-retrieval-benchmark

Implementation of "Audio Retrieval with Natural Language Queries: A Benchmark Study".

Language: Python - Size: 3.07 MB - Last synced at: 10 months ago - Pushed at: almost 3 years ago - Stars: 42 - Forks: 2

oncescuandreea/audio-retrieval

Implementation of "Audio Retrieval with Natural Language Queries", INTERSPEECH 2021, PyTorch

Language: Python - Size: 533 KB - Last synced at: almost 2 years ago - Pushed at: over 3 years ago - Stars: 23 - Forks: 4

dvd125/Classification-of-musical-genres-and-music-retrival

Language: Jupyter Notebook - Size: 8.55 MB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 0 - Forks: 0

dariodellamura/Classification-of-musical-genres-and-music-retrieval

During the project for the DIGITAL SIGNAL IMAGE MANAGEMENT course I learned how to manage and process audio and image files. The aim of the project was the classification, through machine learning and deep learning models, of musical genres by extracting specific audio features from the "gtzan dataset" dataset files with which to train the models (SVM, Linear Regression, Decision tree , Random Forest, Neural Network). Mel spectograms were also extracted to train convolutional neural network models. In addition, the extracted audio features have been used to develop a model of music retrieval which given an audio track in input produces as output 5 audio tracks recommended meiante the use of cousine similarity.

Language: Jupyter Notebook - Size: 8.55 MB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 0 - Forks: 0