We present the NTUA-IRAL team’s solution for the Person Identification track of the Signal Processing EEG-Music Emotion Recognition Grand Challenge, hosted at ICASSP. Our approach employs an ensemble of three CNNs, each pretrained using a distinct strategy: contrastive pre-training, traditional ImageNet pre-training, and task-specific pre-training on a publicly available EEG dataset. This diverse pre-training regimen enabled our models to achieve a test set accuracy of 100%, earning third place in the challenge subtrack.

The introduction of biometric signal analysis in psychiatry could potentially reshape the field by making it more accurate, proactive and personalized. Such biosignals usually acquired from wearables encompass the quantification of human behavior and traits. In this study, we use long-term data acquired from commercial smartwatches, including kinetic and physiological signals, to extract information-thick descriptors that are used for the prediction of subsequent relapses in patients in the psychotic spectrum. Specifically, we propose a novel combination of methods based on Self-Supervised Learning and Survival Analysis that operates on unlabeled and censored data. When combined with other static features that describe the past course of the patient’s health, the proposed methodology yields promising predictive results in terms of two standard survival analysis metrics.

The e-Prevention challenge concerns the analysis and processing of long-term continuous recordings of biosignals recorded from wearable sensors, i.e., accelerometers, gyroscopes and heart rate monitors embedded in smartwatches, as well as sleep information and daily step count, in order to extract high-level representations of the wearer’s activity and behavior, termed as digital phenotypes. The ability of these digital phenotypes to quantify behavioral patterns and traits will be evaluated in two different tasks: 1) Person Identification, and 2) Relapse Detection in patients in the psychotic spectrum. The long-term data that will be used in this challenge have been acquired during the course of the e-Prevention project, an innovative integrated system for medical support that facilitates effective monitoring and relapse prevention in patients with mental disorders (i.e, schizophrenia and bipolar disorder). Specifically, the data were continuously collected from patients for a monitoring period of up to 2.5 years, while from the control subgroup for a period of 3 months, constituting one of the largest of its kind ever recorded.

The study of Music Cognition and neural responses to music has been invaluable in understanding human emotions. Brain signals, though, manifest a highly complex structure that makes processing and retrieving meaningful features challenging, particularly of abstract constructs like affect. Moreover, the performance of learning models is undermined by the limited amount of available neuronal data and their severe inter-subject variability. In this paper we extract efficient, personalized affective representations from EEG signals during music listening. To this end, we employ music signals as a supervisory modality to EEG, aiming to project their semantic correspondence onto a common representation space. We utilize a bi-modal framework by combining an LSTM-based attention model to process EEG and a pre-trained model for music tagging, along with a reverse domain discriminator to align the distributions of the two modalities, further constraining the learning process with emotion tags. The resulting framework can be utilized for emotion recognition both directly, by performing supervised predictions from either modality, and indirectly, by providing relevant music samples to EEG input queries. The experimental findings show the potential of enhancing neuronal data through stimulus information for recognition purposes and yield insights into the distribution and temporal variance of music-induced affective features.

Sound Event Detection and Audio Classification tasks are traditionally addressed through time-frequency representations of audio signals such as spectrograms. However, the emergence of deep neural networks as efficient feature extractors has enabled the direct use of audio signals for classification purposes. In this paper, we attempt to recognize musical instruments in polyphonic audio by only feeding their raw waveforms into deep learning models. Various recurrent and convolutional architectures incorporating residual connections are examined and parameterized in order to build end-to-end classifiers with low computational cost and only minimal preprocessing. We obtain competitive classification scores and useful instrument-wise insight through the IRMAS test set, utilizing a parallel CNN-BiGRU model with multiple residual connections, while maintaining a significantly reduced number of trainable parameters.

In this paper, we describe an interactive generative music system, designed to handle polyphonic guitar music. We formulate the problem of chord progression generation as a prediction problem. Thus, we propose utilization of an LSTM-based network architecture incorporating neural attention that is able to learn a mapping between symbolic representations of polyphonic chord progressions and future chord candidates. Furthermore, we have developed a virtual air-guitar controller, utilizing a Kinect device, that uses the above architecture in order to change in real time the guitar chord mapping, depending on the performer's previous performance. The whole system was evaluated both objectively and subjectively. The goal of the objective evaluation was to measure the ability of the system to correctly generate chord candidates for existing chord progressions, as well as identify the type of errors. The subjective evaluation mainly focused on the longer-term behavior of the system, regarding the musical coherence and the variety of the generated progressions. The results were encouraging regarding the ability of our system to generate sound chord progressions, while highlighting a number of issues that require to be resolved.