Predictive Real-Time Beat Tracking from Music for Embedded Application

Al-Hussaini, Irfan; Humayun, Ahmed Imtiaz; Alam, Samiul; Foysal, Shariful Islam; Masud, Abdullah Al; Arafat, Mahmud; Chowdhury, Rakibul Islam; Ibtehaz, Nabil; Zaman, Sums Uz; Hyder, Rakib; Chowdhury, Sayeed Shafayet; Haque, Mohammad Ariful

doi:10.1109/mipr.2018.00068

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…Furthermore, in 2018, Al-Hussaini presented a beat tracking algorithm in real-time, light enough to be implemented in an embedded system that predicts the pulse with a variant of dynamic programming; including a home memory algorithm to reduce the necessary computational costs. Based on experimental results, the pulse positions of a musical signal in real-time have been satisfactorily demonstrated [20].…”

Section: Literature Reviewmentioning

confidence: 93%

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Intelligent drummer module based on beat-tracking

Villamil¹

2021

ing. Solidar

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Most of the beat-tracking research tends to work on the exploration of theoretical strategies and not on the development of automatic devices that can be functional in real musical environments. As a consequence of the above, there is a scarcity of electronic devices for musical backing based on the beat-tracking technique. So, we have developed an automatic musical backing device based on beat-tracking with the real-time operation.

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Section: Literature Reviewmentioning

confidence: 93%

“…To address the problem mentioned above, the weighted moving average method proposed by [20] is which averages the time values of the beats of the current window to predict the future positions starting from the last beat calculated and estimate the tempo of future rhythms. The following equation calculates future beats:…”

Section: Beats Predictionmentioning

confidence: 99%

Intelligent drummer module based on beat-tracking

Villamil¹

2021

ing. Solidar

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“…Then iteratively updated the beat candidate values and got the predicted beats. In [8] the authors proposed a predictive real-time system using a causal variant of dynamic programming, in which beats are predicted one second in advance. The authors in [9] extended the state-of-the-art offline beat tracking algorithm BeatRoot [10] to a real-time algorithm which adds automatic monitoring and state recovery mechanism to promote beat tracking accuracy and reaction time in the streaming scenario.…”

Section: Introductionmentioning

confidence: 99%

Accelerating real-time music beat tracking based on hidden Markov model by using genre information

Zhou

Song

Wang

et al. 2023

Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022)

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The essence of enjoying music is that people can track music beat anytime and be brought into the scene expressed by music. Music beat tracking is a common task in Music Information Retrieve (MIR). While numerous studies have been done in this field, most works focus on the offline beat tracking. However, tracking music beat in real time is a challenging task for computers. In the past few years, people attach more importance to this field. Researchers care about the music beat without taking music style or context into consideration. In this paper, we propose a method for tracking music beats in real time in conjunction with music genre. Specifically, the proposed model is based on a widely-used framework of Hidden Markov Model (HMM). By recognizing the genre of input music, we narrow the range of beats per minute (BPM), which significantly reduces the number of hidden states in HMM. Consequently, the inference time of beat tracking decreases. We experimentally verify the model on the open-source Ballroom dataset, and its accuracy remains at a competitive level while having a much shorter inference time.

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