ODAQ: Open Dataset of Audio Quality

Research into the prediction and analysis of perceived audio quality is hampered by the scarcity of openly available datasets of audio signals accompanied by corresponding subjective quality scores. To address this problem, we present the Open Dataset of Audio Quality (ODAQ), a new dataset containing the results of a MUSHRA listening test conducted with expert listeners from 2 international laboratories. ODAQ contains 240 audio samples and corresponding quality scores. Each audio sample is rated by 26 listeners. The audio samples are stereo audio signals sampled at 44.1 or 48 kHz and are processed by a total of 6 method classes, each operating at different quality levels. The processing method classes are designed to generate quality degradations possibly encountered during audio coding and source separation, and the quality levels for each method class span the entire quality range. The diversity of the processing methods, the large span of quality levels, the high sampling frequency, and the pool of international listeners make ODAQ particularly suited for further research into subjective and objective audio quality. The dataset is released with permissive licenses, and the software used to conduct the listening test is also made publicly available.

Exploiting spatial information with the informed complex-valued spatial autoencoder for target speaker extraction

In conventional multichannel audio signal enhancement, spatial and spectral filtering are often performed sequentially. In contrast, it has been shown that for neural spatial filtering a joint approach of spectro-spatial filtering is more beneficial. In this contribution, we investigate the influence of the training target on the spatial selectivity of such a time-varying spectro-spatial filter. We extend the recently proposed complex-valued spatial autoencoder (COSPA) for target speaker extraction by leveraging its interpretable structure and purposefully informing the network of the target speaker's position. Consequently, this approach uses a multichannel complex-valued neural network architecture that is capable of processing spatial and spectral information rendering informed COSPA (iCOSPA) an effective neural spatial filtering method. We train iCOSPA for several training targets that enforce different amounts of spatial processing and analyze the network's spatial filtering capacity. We find that the proposed architecture is indeed capable of learning different spatial selectivity patterns to attain the different training targets.

Complex-valued Spatial Autoencoders for Multichannel Speech Enhancement

In this contribution, we present a novel online approach to multichannel speech enhancement. The proposed method estimates the enhanced signal through a filter-and-sum framework. More specifically, complex-valued masks are estimated by a deep complex-valued neural network, termed the complex-valued spatial autoencoder. The proposed network is capable of exploiting as well as manipulating both the phase and the amplitude of the microphone signals. As shown by the experimental results, the proposed approach is able to exploit both spatial and spectral characteristics of the desired source signal resulting in a physically plausible spatial selectivity and superior speech quality compared to other baseline methods.