There are 8 repositories under librosa topic.
NSMusicS,Multi platform Multi mode Music Software ,Electron(Vue3+Vite+TypeScript)+.net core+AI
Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras
Understanding emotions from audio files using neural networks and multiple datasets.
Lightweight and Interpretable ML Model for Speech Emotion Recognition and Ambiguity Resolution (trained on IEMOCAP dataset)
A command-line music video generator based on rhythm
A Machine Learning Approach of Emotional Model
An open-source Python library for audio time-scale modification.
LibrosaCpp is a c++ implemention of librosa to compute short-time fourier transform coefficients,mel spectrogram or mfcc
Python framework for Speech and Music Detection using Keras.
In this project is presented a simple method to train an MLP neural network for audio signals. The trained model can be exported on a Raspberry Pi (2 or superior suggested) to classify audio signal registered with USB microphone
(monophonic) audio to midi converter using Python and librosa
Speech Emotion Recognition (SER) in real-time, using Deep Neural Networks (DNN) of Long Short Memory Term (LSTM).
基于PaddlePaddle实现的音频分类,支持EcapaTdnn、PANNS、TDNN、Res2Net、ResNetSE等各种模型,还有多种预处理方法
Music Synthesis with Python talk, originally given at PyGotham 2017.
Human Emotion Understanding using multimodal dataset.
Music genre classification model using CRNN
一个简单的小网页,录入人声哼唱,转化成钢琴音及钢琴谱输出。灵感稍纵即逝,本项目的目标是能够记录下一段小调,以音频形式输入,读取识别其曲调,并制成谱子,最终以钢琴弹奏的形式输出,依此将一些日常生活中的小灵感保存起来,以便日后回忆甚至再创作。
Image Processing, Speech Processing, Encoder Decoder, Research Paper implementation
Music genre classification from audio spectrograms using deep learning
Environmental sound classification with Convolutional neural networks and the UrbanSound8K dataset.
Sound Classification using Neural Networks
Predicting various emotion in human speech signal by detecting different speech components affected by human emotion.
Classifying English Music (.mp3) files using Music Information Retrieval (MIR), Digital/Audio Signal Processing (DIP) and Machine Learning (ML) Strategies
Artificial intelligence bot for live voice improvisation
MIMII Sound Anomaly Detection with AutoEncoders
Binary classification problem that aims to classify human voices from audio recordings. Implemented using PyTorch and Librosa.
Methods to compute various chroma audio features and audio similarity measures particularly for the task of cover song identification
Automatically sync, mix, and draw virtual choir videos from raw tracks of individual recordings. You may need some singing skills but you don't need video editing skills or additional software.
We'll look into audio categorization using deep learning principles like Artificial Neural Networks (ANN), 1D Convolutional Neural Networks (CNN1D), and CNN2D in this repository. We undertake some basic data preprocessing and feature extraction on audio sources before developing models. As a result, the accuracy, training time, and prediction time of each model are compared. This is explained by model deployment, which allows users to load the desired sound output for each model that is successfully deployed, as will be addressed in more depth later.
Scene Classification using Audio in the nearby Environment.
In this project we use RAVDESS Dataset to classify Speech Emotion using Multi Layer Perceptron Classifier
In this Repository, We developed an audio track separator in tensorflow that successfully separates Vocals and Drums from an input audio song track.