We present a simple and efficient method for beat tracking of musical audio. With the aim of replicating the human ability of tapping in time to music, we formulate our approach using a two state model. The first state performs tempo induction and tracks tempo changes, while the second maintains contextual continuity within a single tempo hypothesis. Beat times are recovered by passing the output of an onset detection function through adaptively weighted comb filterbank matrices to separately identify the beat period and alignment. We evaluate our beat tracker both in terms of the accuracy of estimated beat locations and computational complexity. In a direct comparison with existing algorithms, we demonstrate equivalent performance at significantly reduced computational cost

In order to better understand the musical properties which elicit an increased sensation of wanting to move when listening to music – groove – we investigate the effect of adding syncopation to simple piano melodies, under the hypothesis that syncopation is correlated to groove. Across two experiments we examine listeners’ experience of groove to synthesized musical stimuli covering a range of syncopation levels and densities of musical events, according to formal rules implemented by a computer algorithm that shifts musical events from strong to weak metrical positions. Results indicate that moderate levels of syncopation lead to significantly higher groove ratings than melodies without any syncopation or with maximum possible syncopation. A comparison between the various trans-formations and the way they were rated shows that there is no simple relation between syncopation magnitude and groove.

In this paper, we propose a method that can identify challenging music samples for beat tracking without ground truth. Our method, motivated by the machine learning method “selective sampling,” is based on the measurement of mutual agreement between beat sequences. In calculating this mutual agreement we show the critical influence of different evaluation measures. Using our approach we demonstrate how to compile a new evaluation dataset comprised of difficult excerpts for beat tracking and examine this difficulty in the context of perceptual and musical properties. Based on tag analysis we indicate the musical properties where future advances in beat tracking research would be most profitable and where beat tracking is too difficult to be attempted. Finally, we demonstrate how our mutual agreement method can be used to improve beat tracking accuracy on large music collections.

Groove is a sensation of movement or wanting to move when we listen to certain types of music; it is central to the appreciation of many styles such as Jazz, Funk, Latin, and many more. To better understand the mechanisms that lead to the sensation of groove, we explore the relationship between groove and systematic microtiming deviations. Manifested as small, intentional deviations in timing, systematic microtiming is widely considered within the music community to be a critical component of music performances that groove. To investigate the effect of microtiming on the perception of groove we synthesized typical rhythm patterns for Jazz, Funk, and Samba with idiomatic microtiming deviation patterns for each style. The magnitude of the deviations was parametrically varied from nil to about double the natural level. In two experiments, untrained listeners and experts listened to all combinations of same and different music and microtiming style and magnitude combinations, and rated liking, groove, naturalness, and speed. Contrary to a common and frequently expressed belief in the literature, systematic microtiming led to decreased groove ratings, as well as liking and naturalness, with the exception of the simple short-long shuffle Jazz pattern. A comparison of the ratings between the two listener groups revealed this effect to be stronger for the expert listener group than for the untrained listeners, suggesting that musical expertise plays an important role in the perception and appreciation of microtiming in rhythmic patterns.

This is an extended analysis of eight different algorithms for musical tempo extraction and beat tracking. The algorithms participated in the 2006 Music Information Retrieval Evaluation eXchange (MIREX), where they were evaluated using a set of 140 musical excerpts, each with beats annotated by 40 different listeners. Performance metrics were constructed to measure the algorithms' abilities to predict the most perceptually salient musical beats and tempi of the excerpts. Detailed results of the evaluation are presented here and algorithm performance is evaluated as a function of musical genre, the presence of percussion, musical meter and the most salient perceptual tempo of each excerpt.

In this paper we present a new approach for the generation of multi-instrument symbolic music driven by musical emotion. The principal novelty of our approach centres on conditioning a state-of-the-art transformer based on continuous-valued valence and arousal labels. In addition, we provide a new large-scale dataset of symbolic music paired with emotion labels in terms of valence and arousal. We evaluate our approach in a quantitative manner in two ways, first by measuring its note prediction accuracy, and second via a regression task in the valence-arousal plane. Our results demonstrate that our proposed approaches outperform conditioning using control tokens which is representative of the current state of the art.