Signal Processing and Speech Communication Laboratory


In 2000, the Signal Processing and Speech Communication Laboratory (SPSC Lab) of Graz University of Technology (TU Graz) was founded as a research and education center in nonlinear signal processing and computational intelligence, algorithm engineering, as well as circuits & systems modeling and design. It covers applications in wireless communications, speech/audio communication, and telecommunications.

If you want to learn more about Signal Processing, click: What is Signal Processing?

The Research of SPSC Lab addresses fundamental and applied research problems in five scientific areas:

Result of the Month

Acoustic Scene Classification Using Deep Mixtures Of Pre-trained Convolutional Neural Networks [link]

We propose a heterogeneous system of Deep Mixture of Experts (DMoEs) models using different Convolutional Neural Networks (CNNs) for acoustic scene classification (ASC). Each DMoEs module is a mixture of different parallel CNN structures weighted by a gating network. All CNNs use the same input data. The CNN architectures play the role of experts extracting a variety of features. The experts are pre-trained, and kept fixed (frozen) for the DMoEs model. The DMoEs is post-trained by optimizing weights of the gating network, which estimates the contribution of the experts in the mixture. In order to enhance the performance, we use an ensemble of three DMoEs modules each with different pairs of inputs and individual CNN models. The input pairs are spectrogram combinations of binaural audio and mono audio as well as their pre-processed variations using harmonic-percussive source separation (HPSS) and nearest neighbor filters (NNFs). The classification result of the proposed system is 72.1% improving the baseline by around 12% (absolute) on the development data of DCASE 2018 challenge task 1A.

Contact: Thi Kim Truc Nguyen