Speech separation and localisation using particle filtering
Summary
The objective of this project is to solve the “Cocktail Party Problem”.
Supervisor(s)
Research Location
Electrical and Information Engineering
Program Type
PHD
Synopsis
Humans have a remarkable ability to localise and separate a single speech source in a noisy and cluttered multi-talker environment. This is particularly remarkable when one considers that we only use two ears (sensors) to do so. Steps to solving the "Cocktail Party Problem" include:
- to recover and estimate the original sound source from the observed mixture signals recorded at microphones and
- to localise and track a moving sound source accurately from the observed mixture of signals
Want to find out more?
Contact us to find out what’s involved in applying for a PhD. Domestic students and International students
Contact Research Expert to find out more about participating in this opportunity.
Browse for other opportunities within the Electrical and Information Engineering .
Keywords
Audio Engineering, Signal Processing, Source Separation, Sound localisation, Particle Filtering
Opportunity ID
The opportunity ID for this research opportunity is: 274
Other opportunities with Associate Professor Craig Jin
- Pattern analysis techniques for sound synthesis
- Interpolation of binaural impulse responses for virtual auditory displays
- Sound field recording and recreation
- Beamforming with acoustic vector sensors - Multiple acoustic source localisation using acoustic vector sensor arrays
- Mapping 2D Images to 3D Shape
- New technique for studying human brain activity
- Next Generation Audio Coding
- Spherical multi-modal scene analysis
- Statistical models of ear shape and ear acoustics
- Binaural signal processing algorithms for hearing aids
- Electrical Impedance Tomography for stroke, biophysical monitoring and medical device design
- Impedance tomography for cardiac imaging: high speed tomography
- Medical diagnostics for neonates in the developing world
- Novel Electrodes for rapid electrophysiological recording
- FPGA-based low latency machine learning