Automatic Detection and Classification of tabla talas from the Indian Classical music

! TensorFlow Community Spotlight Winner(First of the program!)

See here

Introduction

This project work aims at developing a system that would be able to first detect a tabla tala from a mix(a song) of an Indian or Carnatic Classical Music and then classify the tala. Tala is a specific pattern which occurs in all of the Indian Classical Music and Tabla is the percussive accompanying instrument.

---

Methods used

1. Models
   • Convolutional Neural Network
   • Long Short-Term Memory Network
2. Signal Processing
   • Downsampling (from 44100 to 16000)
   • Noise Threshold Detection (Using envelope detection)
   • Harmonic-Percussive Source Separation HPSS

---

File description

1. PNstreamsetup.py - Audio backend for live audio and FFT.
2. GUI.py - QtDesigner generated code converted to Python using pyuic
3. Vocalseparation.py - Implementation of vocal separation as shown here.
4. dataops.py - Perform pre-processing on data.
5. model.py - Models and pickles form here.
6. predict.py - Predict on a sample using this.
7. main.py - Real-time visualisations, recording and classification

---

Getting Started

1. Clone this repository git clone https://github.com/pranav6670/Detection-Classification-of-Tabla-taals.git.

2. Data is stored in wavfiles diectory. Check-out the data. There are 10 classes of talas. A test.csv is included in the directory

3. First run the dataops.py script which will pre-process the data which includes, first, downsampling the data and then cleaning it(noise threshold detection).

4. Then create two folders named models and pickles into the directory.

5. Run model.py to form pickles out of the data and start training. The cfg.py can be used to adjust the parameters of MFCC function. In model.py, config.mode == 'conv' will select the CNN model while config.mode == 'rec' will select the LSTM model. The difference between these is only the shape of input data to the model. Once pickles are formed for both the models, the data dosen’t load again the next time, saving alot of time. The models are easy to modify.

6. Under models directory, you will find the conv.model and rec.model formed, after training. Use these models to predict using predict.py

7. Also run main.py after training to do the classification real-time.

---

Tools used for real-time visuals

• Raw and FFT plot

• Spectogram

---

HPSS

---

Data Distribution

---

Features

Time(Raw)	FFT

Filter Bank Energies	MFCC

---

GUI

Made using PyQt5