MusIAC: An Extensible Generative Framework for Music Infilling Applications with Multi-level Control

Guo, Rui; Simpson, Ivor; Kiefer, Chris; Magnusson, Thor; Herremans, Dorien

doi:10.1007/978-3-031-03789-4_22

Cited by 13 publications

(8 citation statements)

References 14 publications

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“…Tonal tension was extracted using the model developed by Herremans and Chew [11] based on the spiral array theory [13]. The model is implemented in the python library midi-miner [27]. The midi input required for the estimation of tonal tension was created using the python package basic-pitch [26] that includes a state-of-the-art polyphonic pitch estimation method.…”

Section: Plos Onementioning

confidence: 99%

TenseMusic: An automatic prediction model for musical tension

Barchet,

Rimmele,

Pelofi

2024

PLoS ONE

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The perception of tension and release dynamics constitutes one of the essential aspects of music listening. However, modeling musical tension to predict perception of listeners has been a challenge to researchers. Seminal work demonstrated that tension is reported consistently by listeners and can be accurately predicted from a discrete set of musical features, combining them into a weighted sum of slopes reflecting their combined dynamics over time. However, previous modeling approaches lack an automatic pipeline for feature extraction that would make them widely accessible to researchers in the field. Here, we present TenseMusic: an open-source automatic predictive tension model that operates with a musical audio as the only input. Using state-of-the-art music information retrieval (MIR) methods, it automatically extracts a set of six features (i.e., loudness, pitch height, tonal tension, roughness, tempo, and onset frequency) to use as predictors for musical tension. The algorithm was optimized using Lasso regression to best predict behavioral tension ratings collected on 38 Western classical musical pieces. Its performance was then tested by assessing the correlation between the predicted tension and unseen continuous behavioral tension ratings yielding large mean correlations between ratings and predictions approximating r = .60 across all pieces. We hope that providing the research community with this well-validated open-source tool for predicting musical tension will motivate further work in music cognition and contribute to elucidate the neural and cognitive correlates of tension dynamics for various musical genres and cultures.

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Section: Plos Onementioning

confidence: 99%

TenseMusic: An automatic prediction model for musical tension

Barchet,

Rimmele,

Pelofi

2024

PLoS ONE

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“…These dependencies are the structures/patterns that we extract from the soundwave. These soundwaves can have various resolution dependencies like different pitch [1], melody, timbre, harmony or rhythm .So a very complex model is needed which is not feasible due to the amount of big data of the training data set. Huge amount of sample data points along the wave amplitude also causes lots of training data to the model.…”

Section: Challenges Of Raw Audiomentioning

confidence: 99%

Orchestrating Melodies through Spectrogram-Driven AutoEncoders

2023

IRJMETS

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The thing that is common to all the people across the world, or the thing that transforms boundaries and connects us all is music. According to statistics every human hears approximately 15 minutes of music every day. Music is one category that all humans consume in all their moods, no matter if they are feeling happy, sad, angry, or lonely. Hence to cater to this need of music generating industry is a multi-billion industry. Everyday millions of songs/music all around the world is being produced and a variety of people like directors, producers, composers, singers, music-writers are included in this lengthy process. Through our research we aim to produce a mock model which will make music creating a less time consuming, lengthy, and costly process. This can be done by using AutoEncoders which are a part of unsupervised neural networks. This machine learning technique will help us generate music, which follows a specific pattern of chords. We aim to create a realistic music tone which is a synthetic data. This can be done by training VAE with Keras and by performing Short-Time/Inverse Short-Time Fourier Transformations.

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“…Transformer networks [37] have been shown to work well for a wide range of MIR tasks [38][39][40][41][42][43][44]. In this paper, we adopt the music tagging transformer proposed in [44] as our musical instrument recognition module, f IR .…”

Section: Instrument Recognition Module F Irmentioning

confidence: 99%

Jointist: Joint Learning for Multi-instrument Transcription and Its Applications

Cheuk¹,

Choi²,

Kong³

et al. 2022

Preprint

Self Cite

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In this paper, we introduce Jointist, an instrument-aware multi-instrument framework that is capable of transcribing, recognizing, and separating multiple musical instruments from an audio clip. Jointist consists of the instrument recognition module that conditions the other modules: the transcription module that outputs instrument-specific piano rolls, and the source separation module that utilizes instrument information and transcription results. The instrument conditioning is designed for an explicit multiinstrument functionality while the connection between the transcription and source separation modules is for better transcription performance.Our challenging problem formulation makes the model highly useful in the real world given that modern popular music typically consists of multiple instruments. However, its novelty necessitates a new perspective on how to evaluate such a model. During the experiment, we assess the model from various aspects, providing a new evaluation perspective for multi-instrument transcription. We also argue that transcription models can be utilized as a preprocessing module for other music analysis tasks. In the experiment on several downstream tasks, the symbolic representation provided by our transcription model turned out to be helpful to spectrograms in solving downbeat detection, chord recognition, and key estimation.

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MusIAC: An Extensible Generative Framework for Music Infilling Applications with Multi-level Control

Cited by 13 publications

References 14 publications

TenseMusic: An automatic prediction model for musical tension

TenseMusic: An automatic prediction model for musical tension

Orchestrating Melodies through Spectrogram-Driven AutoEncoders

Jointist: Joint Learning for Multi-instrument Transcription and Its Applications

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