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School of Electronic Engineering and Computer Science

Mr Cyrus Vahidi


Room Number: Engineering, Eng 104


Project Title: 

Perceptual end to end learning for music understanding


This PhD project is aimed at exploring the synergies between optimal sparse representation of audio signals, psychoacoustics and deep learning. The broader aim is to investigate and to develop novel end-to-end neural network architectures able to emulate low level human hearing tasks including: event detection, source separation, timbre characterization and instrument detection, taking advantage of adaptive and perceptual based signal transformation.

The proposed approach will investigate:

1. the state of the art of harmonic analysis techniques for audio (multiresolution time-frequency and time-scale representation of audio signal).

2. how to use established psychoacoustics models to derive novel perceptual based representations of audio signals.

3. how to use end to end learning techniques to develop novel decomposition of audio signals based on multiresolution analysis and psychoacoustics, taking advantage of the computational efficiency of neural networks;

4. explore applications of the developed methods for various audio task like instrument classification, source separation, event detection, timbre classification.

The research has many industry relevant applications such as intelligent music production and audio transformation. The ideal candidate has good signal processing, machine learning, psychoacoustics background and experience in music production.

C4DM theme affiliation:

Music Informatics


Digital Signal Processing (Undergraduate)

This is a Level 6 module, which builds upon the signal processing theory introduced in ELE374, Signals and Systems Theory. The main part of the module covers the theory of digital signal processing techniques and digital filter design. The module concludes with an examination of some applications of digital signal processing.

Fundamentals of DSP (Postgraduate)

Introduction: Why DSP, sampling, quantization, Signals, LTI systems, Z transforms and polynomials, DFT, FFT, Spectrum Analysis, FIR filters, IIR filters


Research Interests:

Deep Learning, Sound Synthesis, Generative Modelling, Timbre, Auditory Perception, Psychoacoustics

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