bioasplab Goto Github PK
Type: User
Type: User
Bone/Air conducted speech signal enhancement exploiting multi-modal framework
Speech Enhancement based on Denoising Autoencoder with Multi-branched Encoders
Tensorflow implementation for Speech Enhancement (DDAE)
End-to-end waveform utterance enhancement for direct evaluation metrics optimization by fully convolutional neural networks (TASLP 2018)
INCREASING COMPACTNESS OF DEEP LEARNING BASED SPEECH ENHANCEMENT MODELS WITH PARAMETER PRUNING AND QUANTIZATION TECHNIQUES
Investigating the dynamics of biodiversity via passive acoustic monitoring is a challenging task, owing to the difficulty of identifying different animal vocalizations. Several indices have been proposed to measure acoustic complexity and to predict biodiversity. Although these indices perform well under low-noise conditions, they may be biased when environmental and anthropogenic noises are involved. In this paper, we propose a periodicity coded non-negative matrix factorization (PC-NMF) for separating different sound sources from a spectrogram of long-term recordings. The PC-NMF first decomposes a spectrogram into two matrices: spectral basis matrix and encoding matrix. Next, on the basis of the periodicity of the encoding information, the spectral bases belonging to the same source are grouped together. Finally, distinct sources are reconstructed on the basis of the cluster of the basis matrix and the corresponding encoding information, and the noise components are then removed to facilitate more accurate monitoring of biological sounds. Our results show that the PC-NMF precisely enhances biological choruses, effectively suppressing environmental and anthropogenic noises in marine and terrestrial recordings without a need for training data. The results may improve behaviour assessment of calling animals and facilitate the investigation of the interactions between different sound sources within an ecosystem.
Joint Dictionary Learning-based Non-Negative Matrix Factorization for Voice Conversion (TBME 2016)
Python codes for Lite Audio-Visual Speech Enhancement.
MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement (ICML 2019, with Travel awards)
Implementation of "MOSNet: Deep Learning based Objective Assessment for Voice Conversion"
Noise Adaptive Speech Enhancement using Domain Adversarial Training
Official Implementation of SERIL in Pytorch
Tensorflow implementation of the speech model described in Neural Discrete Representation Learning (a.k.a. VQ-VAE)
WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-end Speech Enhancement
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.