167 skills found · Page 1 of 6
jishengpeng / WavTokenizer[ICLR 2025] SOTA discrete acoustic codec models with 40/75 tokens per second for audio language modeling
yizhilll / MERTOfficial implementation of the paper "Acoustic Music Understanding Model with Large-Scale Self-supervised Training".
breizhn / DTLN AecThis Repostory contains the pretrained DTLN-aec model for real-time acoustic echo cancellation.
cmusphinx / SphinxtrainAcoustic model trainer for CMU Sphinx
uhh-lt / Kaldi Tuda DeScripts for training general-purpose large vocabulary German acoustic models for ASR with Kaldi.
aluo-x / Learning Neural Acoustic FieldsOfficial code for "Learning Neural Acoustic Fields" (NeurIPS 2022)
persephone-tools / PersephoneA tool for automatic phoneme transcription
cvqluu / Factorized TDNNPyTorch implementation of the Factorized TDNN (TDNN-F) from "Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks" and Kaldi
daniel-koehn / SAVA3D seismic modelling, FWI and RTM code for wave propagation in isotropic (visco)-acoustic/elastic and anisotropic orthorhombic/triclinic elastic media
bshall / Acoustic ModelAcoustic models for: A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion
SaneBow / PiDTLNApply machine learning model DTLN for noise suppression and acoustic echo cancellation on Raspberry Pi
nilsmorozs / Uwa Channel ModelUnderwater acoustic network modelling based on BELLHOP
orcohen9826 / Acoustic Drones DetectionAn acoustic-based drone detection system using a custom CRNN model and a parabolic microphones. Trained on over 2 million samples, achieving an F1 score above 95%
doans / Underwater Acoustic Target Classification Based On Dense Convolutional Neural NetworkIn oceanic remote sensing operations, underwater acoustic target recognition is always a difficult and extremely important task of sonar systems, especially in the condition of complex sound wave propagation characteristics. Expensively learning recognition model for big data analysis is typically an obstacle for most traditional machine learning (ML) algorithms, whereas convolutional neural network (CNN), a type of deep neural network, can automatically extract features for accurate classification. In this study, we propose an approach using a dense CNN model for underwater target recognition. The network architecture is designed to cleverly re-use all former feature maps to optimize classification rate under various impaired conditions while satisfying low computational cost. In addition, instead of using time-frequency spectrogram images, the proposed scheme allows directly utilizing original audio signal in time domain as the network input data. Based on the experimental results evaluated on the real-world dataset of passive sonar, our classification model achieves the overall accuracy of 98.85$\%$ at 0 dB signal-to-noise ratio (SNR) and outperforms traditional ML techniques, as well as other state-of-the-art CNN models.
egrinstein / RoomfuserAcoustic impulse response generation using diffusion models
qiuk2 / AAR[Official Implementation] Acoustic Autoregressive Modeling 🔥
evelyn0414 / OPERAThis is the official code release for OPERA: OPEn Respiratory Acoustic foundation models
huckiyang / Voice2Series ReprogrammingICML 21 - Voice2Series: Adversarial Reprogramming Acoustic Models for Time Series Classification
gregzanch / Cramcram is a computational room acoustics module to simulate and explore various acoustic properties of a modeled space
org-arl / UnderwaterAcoustics.jlJulia toolbox for underwater acoustic modeling
