In this paper, we present tempo estimation and beat tracking algorithms by utilizing percussive/harmonic separation of the audio signal, in order to extract filterbank energies and chroma features from the respective components. Periodicity analysis is carried out by the convolution of feature sequences with a bank of resonators. Target tempo is estimated from the resulting periodicity vector by incorporating metrical relations knowledge. Tempo estimation is followed by a local tempo refinement method to enhance the beat-tracking algorithm. Beat tracking involves the computation of the beat saliencies derived from the resonators responses and proposes a distance measure between candidate beats locations. A dynamic programming algorithm is adopted to find the optimal “path” of beats. Both tempo estimation and beat tracking methods were submitted on MIREX 2011, while the tempo estimation algorithm was also evaluated on ISMIR 2004 Tempo Induction Evaluation Exchange Dataset.

Automatically synthesizing dance motion sequences is an increasingly popular research task in the broader field of human motion analysis. Recent approaches have mostly used recurrent neural networks (RNNs), which are known to suffer from prediction error accumulation, usually limiting models to synthesize short choreographies of less than 100 poses. In this paper we present a multimodal convolutional autoencoder that combines 2D skeletal and audio information by employing an attention-based feature fusion mechanism, capable of generating novel dance motion sequences of arbitrary length. We first validate the ability of our system to capture the temporal context of dancing in a unimodal setting, by considering only skeletal features as input. According to 1440 rating answers provided by 24 participants in our initial user-study, we show that the optimal performance was presented by the model that was trained with input sequences of 500 poses. Based on this outcome, we train the proposed multimodal architecture with two different approaches, namely teacher-forcing and self-supervised curriculum learning, to deal with the autoregressive error accumulation phenomenon. In our evaluation campaign, we generate 1800 sequences and compare our method against two state-of-the-art approaches. Through qualitative and quantitative experiments we demonstrate the improvements introduced by the proposed multimodal architecture in terms of realism, motion diversity and multimodality, reducing the Fréchet Inception Distance (FID) metric value by 0.39. Subjective results confirm the effectiveness of our approach to synthesize diverse dance motion sequences, reporting a 6% increase in style consistency preference according to 1800 answers provided by 45 evaluators.