Piano Sheet Music Identification Using Dynamic N-gram Fingerprinting

Yang, Daniel; Tsai, TJ

doi:10.5334/tismir.70

Cited by 5 publications

(1 citation statement)

References 5 publications

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“…The type of instrument used in a recording can significantly affect the transcription process due to its distinct characteristics, timbres, and playing techniques associated with different instruments [87]. For example, transcribing piano music involves handling multiple simultaneous notes across a wide range [88,89]. Each musical instrument consists of distinctive timbres, which arise from the combination of the physical attributes and material and playing techniques of singers.…”

Section: Instrumentationmentioning

confidence: 99%

A Comprehensive Review on Music Transcription

Bhattarai,

Lee

2023

Applied Sciences

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Music transcription is the process of transforming recorded sound of musical performances into symbolic representations such as sheet music or MIDI files. Extensive research and development have been carried out in the field of music transcription and technology. This comprehensive review paper surveys the diverse methodologies, techniques, and advancements that have shaped the landscape of music transcription. The paper outlines the significance of music transcription in preserving, analyzing, and disseminating musical compositions across various genres and cultures. It also provides a historical perspective by tracing the evolution of music transcription from traditional manual methods to modern automated approaches. It also highlights the challenges in transcription posed by complex singing techniques, variations in instrumentation, ambiguity in pitch, tempo changes, rhythm, and dynamics. The review also categorizes four different types of transcription techniques, frame-level, note-level, stream-level, and notation-level, discussing their strengths and limitations. It also encompasses the various research domains of music transcription from general melody extraction to vocal melody, note-level monophonic to polyphonic vocal transcription, single-instrument to multi-instrument transcription, and multi-pitch estimation. The survey further covers a broad spectrum of music transcription applications in music production and creation. It also reviews state-of-the-art open-source as well as commercial music transcription tools for pitch estimation, onset and offset detection, general melody detection, and vocal melody detection. In addition, it also encompasses the currently available python libraries that can be used for music transcription. Furthermore, the review highlights the various open-source benchmark datasets for different areas of music transcription. It also provides a wide range of references supporting the historical context, theoretical frameworks, and foundational concepts to help readers understand the background of music transcription and the context of our paper.

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Section: Instrumentationmentioning

confidence: 99%

A Comprehensive Review on Music Transcription

Bhattarai,

Lee

2023

Applied Sciences

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Open^K: An Elastic Data Cleansing System with A Clustering-based Data Anomaly Detection Approach

Dang

Nguyen

Tuan

2021

2021 15th International Conference on Advanced Computing and Applications (ACOMP)

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Large-Scale Multimodal Piano Music Identification Using Marketplace Fingerprinting

Yang¹,

Goutam²,

Ji³

et al. 2022

Algorithms

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This paper studies the problem of identifying piano music in various modalities using a single, unified approach called marketplace fingerprinting. The key defining characteristic of marketplace fingerprinting is choice: we consider a broad range of fingerprint designs based on a generalization of standard n-grams, and then select the fingerprint designs at runtime that are best for a specific query. We show that the large-scale retrieval problem can be framed as an economics problem in which a consumer and a store interact. In our analogy, the runtime search is like a consumer shopping in the store, the items for sale correspond to fingerprints, and purchasing an item corresponds to doing a fingerprint lookup in the database. Using basic principles of economics, we design an efficient marketplace in which the consumer has many options and adopts a rational buying strategy that explicitly considers the cost and expected utility of each item. We evaluate our marketplace fingerprinting approach on four different sheet music retrieval tasks involving sheet music images, MIDI files, and audio recordings. Using a database containing approximately 375,000 pages of sheet music, our method is able to achieve 0.91 mean reciprocal rank with sub-second average runtime on cell phone image queries. On all four retrieval tasks, the marketplace method substantially outperforms previous methods while simultaneously reducing average runtime. We present comprehensive experimental results, as well as detailed analyses to provide deeper intuition into system behavior.

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Piano Sheet Music Identification Using Dynamic N-gram Fingerprinting

Cited by 5 publications

References 5 publications

A Comprehensive Review on Music Transcription

A Comprehensive Review on Music Transcription

Open^K: An Elastic Data Cleansing System with A Clustering-based Data Anomaly Detection Approach

Large-Scale Multimodal Piano Music Identification Using Marketplace Fingerprinting

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Piano Sheet Music Identification Using Dynamic N-gram Fingerprinting

Cited by 5 publications

References 5 publications

A Comprehensive Review on Music Transcription

A Comprehensive Review on Music Transcription

OpenK: An Elastic Data Cleansing System with A Clustering-based Data Anomaly Detection Approach

Large-Scale Multimodal Piano Music Identification Using Marketplace Fingerprinting

Contact Info

Product

Resources

About

Open^K: An Elastic Data Cleansing System with A Clustering-based Data Anomaly Detection Approach