spectrograms

Convmelspec: Convertible Melspectrograms via 1D Convolutions

Easier audio-based machine learning with TensorFlow.

A neural network framework for researchers studying acoustic communication

Environmental sound classification with Convolutional neural networks and the UrbanSound8K dataset.

Deep learning using CNN for Mandarin Chinese tone classification

NTU RGB+D Dataset Action Recognition with GNNs and CNNs

Streamline your ecoacoustic analysis with LEAVES, offering advanced tools for large-scale soundscape annotation and visualization. Join researchers and citizen scientists using LEAVES to analyze complex soundscapes faster and more accurately.

Find gravitational wave signals from binary black hole collisions.

Using deep learning techniques like 1D and 2D CNNs, LSTM to detect damage in a structure with hinges/joints after an earthquake.

Music timbre transfer

NoiseCapture 2 Multi Platform

Tackle accent classification and conversion using audio data, leveraging MFCCs and spectrograms. Models differentiate accents and convert audio between accents

Code and material relevant to the paper "Spectrogrand: Computational Creativity Driven Audiovisuals' Generation From Text Prompts"

Classifying Radio signal coming from space

TinyML project. This system monitors your room or surrounding with an onboard microphone of Arduino nano BLE sense. Still Under Developement

Bachelor Final Year Project exploring real-time speech denoising using machine learning. Compares classical methods (SS, WF, MMSE-LSA) with 5 deep models on spectrogram data, highlighting Conv-TasNet’s effectiveness. Features dataset bucketing, OOM mitigation, and batch evaluation.

LOFAR System Health Management

A Python script that transforms images into audio that I made overnight

Analysis of human behavioral and neural variability during naturalistic arm movements. Replicates the findings in our preprint: https://www.biorxiv.org/content/10.1101/2020.04.17.047357v2

Digital Signal Processing Projects