#Librosa
Showing 12 of 12 repositories tagged #librosa, ranked by stars
Python library for audio and music analysis
AudioMuse-AI uses sonic analysis to rediscover forgotten songs, uncover hidden connections in your music library, and generate intelligent playlists for Jellyfin, Navidrome, LMS, Lyrion, and Emby: no metadata or external services required.
Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras
Lightweight and Interpretable ML Model for Speech Emotion Recognition and Ambiguity Resolution (trained on IEMOCAP dataset)
Understanding emotions from audio files using neural networks and multiple datasets.
A Machine Learning Approach of Emotional Model
Speech Emotion Recognition (SER) in real-time, using Deep Neural Networks (DNN) of Long Short Memory Term (LSTM).
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
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。
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.
Predicting various emotion in human speech signal by detecting different speech components affected by human emotion.
ADHD_Recognition with personal voice