A Novel Melody Line Identification Algorithm for Polyphonic MIDI Music

Velusamy, Sudha; Thoshkahna, Balaji; Ramakrishnan, K. R.

doi:10.1007/978-3-540-69429-8_25

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…Track and measure level methods The early MIDI-based main melody extraction methods are essentially extracting the MIDI track. Specifically, handcrafted (HC) features of the notes' attributes are adopted as the metrics to select a track containing the main melody notes [12,15,16,[19][20][21]31]. Traditional metrics utilized to filter out nonmain melody notes include the entropy and average of pitch, the standard deviation of duration [32].…”

Section: Midi-based Methodsmentioning

confidence: 99%

“…For a piece of piano music, the corresponding MIDI number of absolute pitch values range from 21 to 108. To represent the chord events, we use a chord recognition method proposed by Huang and Yang [54], which basically uses a sliding window and proposes a rule of calculating chord Ozcan [12], Friberg [32], Li [35] U --U -Martin [21], Chen [22], Rizo [19], Velusamy [20] U ---U Jiang [34], Adiloglu [36] -…”

Section: Data Representationmentioning

confidence: 99%

“…Conventional methods use the most distinctive feature of MIDI files (track message) to obtain the main melody [12,16,[19][20][21][22]. These methods assume that the main melody notes are located on a single track, and therefore, they directly identify the main melody track using some statistical characteristics of the track of interest.…”

Section: Introductionmentioning

confidence: 99%

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Multi-mmlg: a novel framework of extracting multiple main melodies from MIDI files

Zhao

Taniar

Adhinugraha

et al. 2023

Neural Comput & Applic

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As an essential part of music, main melody is the cornerstone of music information retrieval. In the MIR’s sub-field of main melody extraction, the mainstream methods assume that the main melody is unique. However, the assumption cannot be established, especially for music with multiple main melodies such as symphony or music with many harmonies. Hence, the conventional methods ignore some main melodies in the music. To solve this problem, we propose a deep learning-based Multiple Main Melodies Generator (Multi-MMLG) framework that can automatically predict potential main melodies from a MIDI file. This framework consists of two stages: (1) main melody classification using a proposed MIDIXLNet model and (2) conditional prediction using a modified MuseBERT model. Experiment results suggest that the proposed MIDIXLNet model increases the accuracy of main melody classification from 89.62 to 97.37%. In addition, this model requires fewer parameters (71.8 million) than the previous state-of-art approaches. We also conduct ablation experiments on the Multi-MMLG framework. In the best-case scenario, predicting meaningful multiple main melodies for the music are achieved.

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Section: Midi-based Methodsmentioning

confidence: 99%

Section: Data Representationmentioning

confidence: 99%

See 1 more Smart Citation

Multi-mmlg: a novel framework of extracting multiple main melodies from MIDI files

Zhao

Taniar

Adhinugraha

et al. 2023

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…However, there are other, non-melody tracks within MIDI files. Traditional melody extraction research includes Skyline [29], Best Channel [30], Top Channel [31], Entropy Channel [32], Entropy Part [33], and Revised Skyline [34]. The most common method of extracting melody tracks of extracting melody tracks from MIDI files is the Skyline algorithm [26], which combines all notes into a single track and selects the highest pitch sounds (typically a representative feature of the melody portions of a musical piece).…”

Section: Related Literaturementioning

confidence: 99%

Melody Extraction and Encoding Method for Generating Healthcare Music Automatically

Jang

Sung

2019

Electronics

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The strong relationship between music and health has helped prove that soft and peaceful classical music can significantly reduce people’s stress; however, it is difficult to identify and collect examples of such music to build a library. Therefore, a system is required that can automatically generate similar classical music selections from a small amount of input music. Melody is the main element that reflects the rhythms and emotions of musical works; therefore, most automatic music generation research is based on melody. Given that melody varies frequently within musical bars, the latter are used as the basic units of composition. As such, there is a requirement for melody extraction techniques and bar-based encoding methods for automatic generation of bar-based music using melodies. This paper proposes a method that handles melody track extraction and bar encoding. First, the melody track is extracted using a pitch-based term frequency–inverse document frequency (TFIDF) algorithm and a feature-based filter. Subsequently, four specific features of the notes within a bar are encoded into a fixed-size matrix during bar encoding. We conduct experiments to determine the accuracy of track extraction based on verification data obtained with the TFIDF algorithm and the filter; an accuracy of 94.7% was calculated based on whether the extracted track was a melody track. The estimated value demonstrates that the proposed method can accurately extract melody tracks. This paper discusses methods for automatically extracting melody tracks from MIDI files and encoding based on bars. The possibility of generating music through deep learning neural networks is facilitated by the methods we examine within this work. To help the neural networks generate higher quality music, which is good for human health, the data preprocessing methods contained herein should be improved in future works.

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