Algorithms for musical tempo estimation have become increasingly complicated in recent years. These algorithms typically utilize two fundamental properties of musical rhythm: some features of the audio signal are self-similar at periods related to the underlying rhythmic structure, and rhythmic events tend to be spaced regularly in time. We present a streamlined tempo estimation method ( stem) that distills ideas from previous work by reducing the number of steps, parameters, and modeling assumptions while retaining good accuracy. This method is designed for music with a constant or near-constant tempo. The proposed method either outperforms or has similar performance to many existing state-of-the-art algorithms. Self-similarity is captured through autocorrelation of the onset strength signal (OSS), and time regularity is captured through cross-correlation of the OSS with regularly spaced pulses. Our findings are supported by the most comprehensive evaluation of tempo estimation algorithms to date in terms of the number of datasets and tracks considered. During the process we have also corrected ground truth annotations for the datasets considered. All the data, the annotations, the evaluation code, and three different implementations (C++, Python, MATLAB) of the proposed algorithm are provided in order to support reproducibility.

Tempo estimation is a fundamental problem in music information retrieval. It also forms the basis of other types of rhythmic analysis such as beat tracking and pattern detection. There is a large body of work in tempo estimation using a variety of different approaches that differ in their accuracy as well as their complexity. Fundamentally they take advantage of two properties of musical rhythm: 1) the music signal tends to be self-similar at periodicities related to the underlying rhythmic structure, 2) rhythmic events tend to be spaced regularly in time. We propose an algorithm for tempo estimation that is based on these two properties. We have tried to reduce the number of steps, parameters and modeling assumptions while retaining good performance and causality. The proposed approach outperforms a large number of existing tempo estimation methods and has similar performance to the best-performing ones. We believe that we have conducted the most comprehensive evaluation to date of tempo induction algorithms in terms of number of datasets and tracks.

This work improves the realism of synthesis and performance of string quartet music by generating audio through physical modelling of the violins, viola, and cello. To perform music with the physical models, virtual musicians interpret the musical score and generate actions which control the physical models. The resulting audio and haptic signals are examined with support vector machines, which adjust the bowing parameters in order to establish and maintain a desirable timbre. This intelligent feedback control is trained with human input, but after the initial training is completed, the virtual musicians perform autonomously. The system can synthesize and control different instruments of the same type (e.g., multiple distinct violins) and has been tested on two distinct string quartets (total of 8 violins, 2 violas, 2 cellos). In addition to audio, the system creates a video animation of the instruments performing the sheet music.

Composition, listening, and performance are essential activities in classroom music education, yet conventional music classes impose unnecessary limitations on students' ability to develop these skills. Based on in-depth fieldwork and a user-centered design approach, we created MOGCLASS, a multimodal collaborative music environment that enhances students' musical experience and improves teachers' management of the classroom.

We introduce MOGCLASS: a system of networked mobile devices to amplify and extend children's capabilities to perceive, perform and produce music collaboratively in classroom context. MOGCLASS includes various features for students to enhance their motivation, interest, and collaboration in music class. It provides a wide-ranging palette of easy-to-use musical instruments for students to choose from, and supports both collaborative silent practice with headphones, and collaborative performance with loudspeakers. To facilitate classroom management, the teacher's interface is used to control students' activities. Our evaluation results indicate that MOGCLASS is effective in increasing students' motivation in learning music and in supporting teachers' classroom management

The computational power and sensory capabilities of mobile devices are increasing dramatically these days, rendering them suitable for real-time sound synthesis and various musical expressions. In this paper, we demonstrate a novel mobile music making system which leverages the ubiquity, ultra-mobility, and multi-modality of mobile devices (iPod touch) for people to create and compose music collaboratively. Unlike the conventional music making applications which generate the music on a single mobile device with a preset sound and interface, our system allows several players in a group to be connected together through wireless LAN network, creating music with different sounds and interfaces. Finally, the performance can be recorded as a single music file and played back in the future. The paper also shows some application scenarios for this collaborative music making system in future research.