Audio signal processing

Our research topics and projects are related to headset signal processing, loudspeakers, sound synthesis, effects processing algorithms, digital filters, musical instruments, acoustic measurements, subjective evaluation, and interactive audio systems. The emphasis of our research is on digital signal processing and machine learning methods which account for the properties of human hearing. We have collaborated with several companies, such as Genelec and Nokia Technologies. Much of our academic research is conducted in collaboration with foreign partners, such as Aalborg University, The University of Edinburgh, and Stanford University.
This research group organizes the Audio Signal Processing course at Aalto University annually in January-April. We also arrange the Audio Technology Seminar every second year (February-May). Both courses are suitable for Master's and doctoral students, who have a solid background in acoustics and digital signal processing. Additionally, we are responsible for the Noise Control course for Master's and doctoral students and for the Sound and Speech Processing course (Äänen- ja puheenkäsittely, in Finnish) for BSc students.
The Audio Signal Processing Research Group belongs to the Aalto Acoustics Lab. The research group is led by Professor Vesa Välimäki, IEEE Fellow, AES Fellow.
- Artificial reverberation
- Audio filter design (equalizers, delay filters)
- Audio headsets and augmented reality audio
- Deep learning for audio processing
- Digital sound synthesis
- Loudspeaker signal processing
- Time-scale modification of sound
- Virtual analog modeling for music technology
Group members
Latest publications
Crossover Networks: A Review
Differentiable Feedback Delay Network For Colorless Reverberation
Improved Aures tonality metric for complex sounds
Enhanced Fuzzy Decomposition of Sound Into Sines, Transients, and Noise
Extreme Audio Time Stretching Using Neural Synthesis
Neural Modeling of Magnetic Tape Recorders
Neural Grey-Box Guitar Amplifier Modelling With Limited Data
BEHM-GAN: Bandwidth Extension of Historical Music using Generative Adversarial Networks
Solving Audio Inverse Problems with a Diffusion Model
Rule of two: methods to detect and localize non-stationary noise in sweep measurements
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