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The goal of live song identification is to recognize a song based on a short, noisy cell phone recording of a live performance. We propose a system for known-artist live song identification and provide empirical evidence of its feasibility. The proposed system represents audio as a sequence of hashprints, which are binary fingerprints that are derived from applying a set of spectro-temporal filters to a spectrogram representation. The spectro-temporal filters can be learned in an unsupervised manner on a small amount of data, and can thus tailor its representation to each artist. Matching is performed using a cross-correlation approach with downsampling and rescoring. We evaluate our approach on the Gracenote live song identification benchmark data set, and compare our results to five other baseline systems. Compared to the previous state-of-the-art, the proposed system improves the mean reciprocal rank from .68 to .79, while simultaneously reducing the average runtime per query from 10 seconds down to 0.9 seconds. [ABSTRACT FROM AUTHOR]

This paper addresses the question how music information retrieval techniques originally developed to process audio recordings can be adapted for the analysis of corresponding brain activity data. In particular, we conducted a case study applying beat tracking techniques to extract the tempo from electroencephalography (EEG) recordings obtained from people listening to music stimuli. We point out similarities and differences in processing audio and EEG data and show to which extent the tempo can be successfully extracted from EEG signals. Furthermore, we demonstrate how the tempo extraction from EEG signals can be stabilized by applying different fusion approaches on the mid-level tempogram features. [ABSTRACT FROM AUTHOR]