Real-time Beat Tracking Research Articles

A Real-Time Beat Tracking System with Zero Latency and Enhanced Controllability

A Real-Time Beat Tracking System with Zero Latency and Enhanced Controllability

Drumming up Interest in Signal Processing

The 2017 IEEE Signal Processing Cup required us to implement real-time beat tracking on an embedded system with a creative application. Our team built an electromechanical drum kit capable of accompanying live musicians. A major difficulty of real-time beat tracking is the tradeoff between adaptability and stability of the output. This can be a real hassle when trying to use the drum kit in a live performance. This project also went beyond the competition, with the implementation of a drum sequencer and an offline beat tracker/downbeat detector.

OBTAIN: Real-Time Beat Tracking in Audio Signals

In this paper, we design a system in order to perform the real-time beat tracking for an audio signal. We use Onset Strength Signal (OSS) to detect the onsets and estimate the tempos. Then, we form Cumulative Beat Strength Signal (CBSS) by taking advantage of OSS and estimated tempos. Next, we perform peak detection by extracting the periodic sequence of beats among all CBSS peaks. In simulations, we can see that our proposed algorithm, Online Beat TrAckINg (OBTAIN), outperforms state-of-art results in terms of prediction accuracy while maintaining comparable and practical computational complexity. The real-time performance is tractable visually as illustrated in the simulations.

Audio-Visual Beat Tracking Based on a State-Space Model for a Robot Dancer Performing with a Human Dancer

[abstFig src='/00290001/12.jpg' width='300' text='An overview of real-time audio-visual beat-tracking for music audio signals and human dance moves' ] This paper presents a real-time beat-tracking method that integrates audio and visual information in a probabilistic manner to enable a humanoid robot to dance in synchronization with music and human dancers. Most conventional music robots have focused on either music audio signals or movements of human dancers to detect and predict beat times in real time. Since a robot needs to record music audio signals with its own microphones, however, the signals are severely contaminated with loud environmental noise. To solve this problem, we propose a state-space model that encodes a pair of a tempo and a beat time in a state-space and represents how acoustic and visual features are generated from a given state. The acoustic features consist of tempo likelihoods and onset likelihoods obtained from music audio signals and the visual features are tempo likelihoods obtained from dance movements. The current tempo and the next beat time are estimated in an online manner from a history of observed features by using a particle filter. Experimental results show that the proposed multi-modal method using a depth sensor (Kinect) to extract skeleton features outperformed conventional mono-modal methods in terms of beat-tracking accuracy in a noisy and reverberant environment.

Beat Tracking for Interactive Dancing Robots

Dance movement is intrinsically connected to the rhythm of music and is a fundamental form of nonverbal communication present in daily human interactions. In order to enable robots to interact with humans in natural real-world environments through dance, these robots must be able to listen to music while robustly tracking the beat of continuous musical stimuli and simultaneously responding to human speech. In this paper, we propose the integration of a real-time beat tracking system with state recovery with different preprocessing solutions used in robot audition for its application to interactive dancing robots. The proposed system is assessed under different real-world acoustic conditions of increasing complexity, which consider multiple audio sources of different kinds, multiple noise sources of different natures, continuous musical and speech stimuli, and the effects of beat-synchronous ego-motion noise and of jittering in ego noise (EN). The overall results suggest improved beat tracking accuracy with lower reaction times to music transitions, while still enhancing automatic speech recognition (ASR) run in parallel in the most challenging conditions. These results corroborate the application of the proposed system for interactive dancing robots.

Modification of the algorithm for beat tracking of a musical melody

A new efficient modification of the known heuristic algorithm for real-time beat tracking is proposed. An improved formula for updating the relative frequencies of time intervals between adjacent onsets is used in the modification. The algorithm has shown good performance on the MIREX Beat Tracking test base.

BBCut2: Integrating beat tracking and on-the-fly event analysis

BBCut2 is the latest manifestation of a software library for realtime algorithmic audio splicing. Machine listening capabilities are supported for realtime beat tracking and audio event analysis, such that splicing manipulations respect component events, and their micro-timing with respect to an inferred metrical structure. The architecture, whilst currently most effective for transient rich percussive signals, is modular enough to be extensible to new observation models. A scheduling system is described that can cope with splicing driven from an external clock, empowering realtime beat tracking led segmentation and other processing effects.

An Audio-based Real-time Beat Tracking System for Music With or Without Drum-sounds

This paper describes a real-time beat tracking system that recognizes a hierarchical beat structure comprising the quarter-note, half-note, and measure levels in real-world audio signals sampled from popular-music compact discs. Most previous beat-tracking systems dealt with MIDI signals and had difficulty in processing, in real time, audio signals containing sounds of various instruments and in tracking beats above the quarter-note level. The system described here can process music with drums and music without drums and can recognize the hierarchical beat structure by using three kinds of musical knowledge: of onset times, of chord changes, and of drum patterns. This paper also describes several applications of beat tracking, such as beat-driven real-time computer graphics and lighting control.

Real-time beat tracking for drumless audio signals: Chord change detection for musical decisions

This paper describes a real-time beat-tracking system that detects a hierarchical beat structure in musical audio signals without drum-sounds. Most previous systems have dealt with MIDI signals and had difficulty in applying, in real time, musical heuristics to audio signals containing sounds of various instruments and in tracking beats above the quarter-note level. Our system not only tracks beats at the quarter-note level but also detects beat structure at the half-note and measure levels. To make musical decisions about the audio signals, we propose a method of detecting chord changes that does not require chord names to be identified. The method enables the system to track beats at different rhythmic levels – for example, to find the beginnings of half notes and measures – and to select the best of various hypotheses about beat positions. Experimental results show that the proposed method was effective to detect the beat structure in real-world audio signals sampled from compact discs of popular music.