tango4j / music-noise-segmentation-on-a-spectrogram
MNSS (Music Noise Segmentation on a Spectrogram) is a deep-neural network based preprocessing technique that pre-filters unnecessary noise. MNSS is based on the convolutional neural networks and uses softmax value as a probability of noise existence.
☆11Updated 9 years ago
Alternatives and similar repositories for music-noise-segmentation-on-a-spectrogram:
Users that are interested in music-noise-segmentation-on-a-spectrogram are comparing it to the libraries listed below
- a music segmentation algorithm that I proposed and implemented as my undergraduate project. The basic function is: a song is loaded to th…☆15Updated 11 years ago
- singing voice analysis and detection tools☆21Updated 9 years ago
- ☆8Updated 6 years ago
- Deep Convolutional Networks on the Pitch Spiral for Musical Instrument Recognition☆41Updated 8 years ago
- Repository containg experiments with Extreme Learning Machines And Reservoir Computing, ELMARC.☆20Updated 6 years ago
- Segmentation algorithm for MIREX 2014☆14Updated 9 years ago
- Music structure segmentation with convnets☆13Updated 8 years ago
- Support material and source code for the system described in : "New Sonorities for Jazz Recordings: Separation and Mixing using Deep Neu…☆13Updated 7 years ago
- sliding HPSS and two stage HPSS (singing voice enhancement)☆16Updated 4 years ago
- ☆25Updated 6 years ago
- Deep Recurrent Neural Network for Audio Source Separation☆37Updated 9 years ago
- Audio Processing Seminar 2016: Reproducible Research☆20Updated 7 years ago
- ☆17Updated 4 years ago
- Single Pass Spectrogram Inversion in a Jupyter Python notebook☆33Updated 7 years ago
- Melody extraction based on source-filter modelling☆26Updated 5 years ago
- NSynth for the rest of us☆14Updated 7 years ago
- Implementation of the YAAPT (Yet Another Algorithm for Pitch Tracking), an algorithm that determines the fundamental frequency of noisy s…☆15Updated 10 years ago
- Zero-Mean Convolutions for Level-Invariant Singing Voice Detection☆11Updated 6 years ago
- The phoneme classification code for EUSIPCO 2017 paper: Timbre Analysis of Music Audio Signals with Convolutional Neural Networks☆20Updated 7 years ago
- Library for research in audio analysis, processing and synthesis☆42Updated 8 years ago
- ☆21Updated 6 years ago
- A framework for overviewing the performance of F0 estimators☆19Updated 8 years ago
- ☆8Updated 7 years ago
- Consistent dictionary learning algorithm for signal declipping (Python code)☆19Updated 6 years ago
- Parse and process the demixing secrets dataset (DSD100)☆49Updated 6 years ago
- Training neural audio classifiers with few data − https://arxiv.org/abs/1810.10274☆60Updated 6 years ago
- Auralisation of learned features in CNN (for audio)☆42Updated 7 years ago
- Experimenting with musically motivated convolutional neural networks☆16Updated 8 years ago
- Support material and source code for the model described in : "A Recurrent Encoder-Decoder Approach With Skip-Filtering Connections For M…☆13Updated 7 years ago
- Multiple Fundamental Frequency Estimation☆26Updated 10 years ago