Abstract
Within ballroom dance music, tempo and rhythmic style are strongly related. In this paper we explore this relationship, by using knowledge of rhythmic style to improve tempo estimation in musical audio signals. We demonstrate how the use of a simple 1-NN classification method, able to determine rhythmic style with 75% accuracy, can lead to an 8% point improvement over existing tempo estimation algorithms with further gains possible through the use of more sophisticated classification techniques.

Abstract
Hardcore, jungle, and drum and bass (HJDB) are fast-paced electronic dance music genres that often employ resequenced breakbeats or drum samples from jazz and funk percussionist solos. We present a style-specific method for downbeat detection specifically designed for HJDB. The presented method combines three forms of metrical information in the prediction of downbeats: low-level onset event information; periodicity information from beat tracking; and high-level information from a regression model trained with classic breakbeats. In an evaluation using 206 HJDB pieces, we demonstrate superior accuracy of our style specific method over four general downbeat detection algorithms. We present this result to motivate the need for style-specific knowledge and techniques for improved downbeat detection. © 2012 International Society for Music Information Retrieval.

Abstract
In this paper we establish a threshold for perceptually acceptable beat tracking based on the mutual agreement of a committee of beat trackers. In the first step we use an existing annotated dataset to show that mutual agreement can be used to select one committee member as the most reliable beat tracker for a song. Then we conduct a listening test using a subset of the Million Song Dataset to establish a threshold which results in acceptable quality of the chosen beat output. For both datasets, we obtain a percentage of trackable music of about 73%, and we investigate which data tags are related to acceptable and problematic beat tracking. The results indicate that current datasets are biased towards genres which tend to be easy for beat tracking. The proposed methods provide a means to automatically obtain a confidence value for beat tracking in non-annotated data and to choose between a number of beat tracker outputs. © 2012 International Society for Music Information Retrieval.

Abstract
In this paper we propose an audio beat tracking system, IBT, for multiple applications. The proposed system integrates an automatic monitoring and state recovery mechanism, that applies (re-)inductions of tempo and beats, on a multi-agent-based beat tracking architecture. This system sequentially processes a continuous onset detection function while propagating parallel hypotheses of tempo and beats. Beats can be predicted in a causal or in a non-causal usage mode, which makes the system suitable for diverse applications. We evaluate the performance of the system in both modes on two application scenarios: standard (using a relatively large database of audio clips) and streaming (using long audio streams made up of concatenated clips). We show experimental evidence of the usefulness of the automatic monitoring and state recovery mechanism in the streaming scenario (i.e., improvements in beat tracking accuracy and reaction time). We also show that the system performs efficiently and at a level comparable to state-of-the-art algorithms in the standard scenario. IBT is multi-platform, open-source and freely available, and it includes plugins for different popular audio analysis, synthesis and visualization platforms.

Abstract
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.

Abstract
A new probabilistic framework for beat tracking of musical audio is presented. The method estimates the time between consecutive beat events and exploits both beat and non-beat information by explicitly modeling non-beat states. In addition to the beat times, a measure of the expected accuracy of the estimated beats is provided. The quality of the observations used for beat tracking is measured and the reliability of the beats is automatically calculated. A k-nearest neighbor regression algorithm is proposed to predict the accuracy of the beat estimates. The performance of the beat tracking system is statistically evaluated using a database of 222 musical signals of various genres. We show that modeling non-beat states leads to a significant increase in performance. In addition, a large experiment where the parameters of the model are automatically learned has been completed. Results show that simple approximations for the parameters of the model can be used. Furthermore, the performance of the system is compared with existing algorithms. Finally, a new perspective for beat tracking evaluation is presented. We show how reliability information can be successfully used to increase the mean performance of the proposed algorithm and discuss how far automatic beat tracking is from human tapping.