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Explainable Artificial Intelligence (XAI) is targeted at understanding how models perform feature selection and derive their classification decisions. This paper explores post-hoc explanations for deep neural networks in the audio domain. Notably, we present a novel Open Source audio dataset consisting of 30,000 audio samples of English spoken digits which we use for classification tasks on spoken digits and speakers' biological sex. We use the popular XAI technique Layer-wise Relevance Propagation LRP to identify relevant features for two neural network architectures that process either waveform or spectrogram representations of the data. Based on the relevance scores obtained from LRP, hypotheses about the neural networks' feature selection are derived and subsequently tested through systematic manipulations of the input data. Further, we take a step beyond visual explanations and introduce audible heatmaps. We demonstrate the superior interpretability of audible explanations over visual ones in a human user study. • We present a novel audio dataset consisting of 30,000 audio samples of spoken digits. • We use LRP to explain predictions of two different models in the audio domain. • We confirm hypotheses about the neural networks' use of features from explanations. • We present audible explanations and demonstrate their superior interpretability. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to advancing artificial intelligence (AI) through the development of the Complex Recurrent Spectral Network (ℂ -RSN), an innovative variant of the Recurrent Spectral Network (RSN) model. The ℂ -RSN model introduces localized non-linearity, complex fixed eigenvalues, and a distinct separation of memory and input processing functionalities. These features enable the ℂ -RSN to evolve towards a dynamic, oscillating final state that bear some degree of similarity with biological cognition. The model's ability to classify data through a time-dependent function, and the localization of information processing, is demonstrated by using the MNIST dataset. Remarkably, distinct items supplied as a sequential input yield patterns in time which bear the indirect imprint of the insertion order (and of the separation in time between contiguous insertions). • The C-RSN overcomes some limitations in existing neural network models. • C-RSN introduces localized non-linearity, complex eigenvalues and a separated memory. • The network evolves towards a dynamic, oscillating final state. • The model's efficacy is demonstrated through empirical evaluation. • C-RSN is able to process sequential inputs keeping track of the insertion order. [ABSTRACT FROM AUTHOR]