SalsaAsst: Beat Counting System Empowered by Mobile Devices to Assist Salsa Dancers

Dancing is always challenging especially for beginners who may lack sense of rhythm. Salsa, as a popular style of dancing, is even harder to learn due to its unique overlapped rhythmic patterns made by different Latin instruments (e.g., Clave sticks, Conga drums, Timbale drums) together. In order to dance in synchronization with the Salsa beats, the beginners always need prompts (e.g., beat counting voice) to remind them of the beat timing. The traditional way to generate the Salsa music with beat counting voice prompts requires professional dancers or musicians to count Salsa beats manually, which is only possible in dance studios. Additionally, the existing music beat tracking solutions cannot well capture the Salsa beats due to its intricacy of rhythms. In this work, we propose a mobile device enabled beat counting system, SalsaAsst, which can perform rhythm deciphering and fine-grained Salsa beat tracking to assist Salsa dancers with beat counting voice/vibration prompts. The proposed system can be used conveniently in many scenarios, which can not only help Salsa beginners make accelerated learning progress during practice at home but also significantly reduce professional dancers' errors during their live performance. The developed Salsa beat counting algorithm has the capability to track beats accurately in both real-time and offline manners. Our extensive tests using 40 Salsa songs under 8 evaluation metrics demonstrate that SalsaAsst can accurately track the beats of Salsa music and achieve much better performance comparing to existing beat tracking approaches.

[1]  D. Ellis Beat Tracking by Dynamic Programming , 2007 .

[2]  Matthew E. P. Davies,et al.  On the Use of Entropy for Beat Tracking Evaluation , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[3]  Tetsuya Ogata,et al.  A robot listens to music and counts its beats aloud by separating music from counting voice , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Geoffroy Peeters,et al.  Template-Based Estimation of Time-Varying Tempo , 2007, EURASIP J. Adv. Signal Process..

[5]  Eric D. Scheirer,et al.  Tempo and beat analysis of acoustic musical signals. , 1998, The Journal of the Acoustical Society of America.

[6]  Peter Desain,et al.  On tempo tracking: Tempogram Representation and Kalman filtering , 2000, ICMC.

[7]  C E Rice,et al.  Human Echo Perception , 1967, Science.

[8]  Priscilla Renta Salsa Dance:Latino/ a History in Motion , 2004 .

[9]  Matthew E. P. Davies,et al.  Evaluation of Audio Beat Tracking and Music Tempo Extraction Algorithms , 2007 .

[10]  Yoshinori Kuno,et al.  Multimodal presentation method for a dance training system , 2005, CHI Extended Abstracts.

[11]  Katsutoshi Itoyama,et al.  Audio-visual beat tracking based on a state-space model for a music robot dancing with humans , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[12]  Dieter Drobny,et al.  Saltate!: a sensor-based system to support dance beginners , 2009, CHI Extended Abstracts.

[13]  Matthew E. P. Davies,et al.  Context-Dependent Beat Tracking of Musical Audio , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[14]  Jyh-Shing Roger Jang,et al.  A Two-Fold Dynamic Programming Approach to Beat Tracking for Audio Music with Time-Varying Tempo , 2011, ISMIR.