Scene2Wav: a deep convolutional sequence-to-conditional SampleRNN for emotional scene musicalization

Sergio, Gwenaelle Cunha; Lee, Minho

doi:10.1007/s11042-020-09636-5

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…Secondary information should be ignored to make the generated notes more accurate. e self-attention mechanism can also assign probability weights to corresponding contextual note features, deeply mine the dependent features in the note sequence, and achieve accurate expression of the note sequence, which makes the prediction and generation of note sequence more accurate as well as ensures the validity of music generation [18]. Figure 3 shows the structure of composing model built in this paper.…”

Section: Build Composing Models Context Information Can Affectmentioning

confidence: 99%

A Deep Learning-Enabled Composition System Based on Piano Score Recognition

Shang

2022

Mobile Information Systems

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Piano is used for music and comprises a stringed keyboard instrument wherein the strings are tapped by softer-coated wooden hammers. The score providing music for the piano, often a compressed transcription of orchestral music, is referred to as piano score. Presently, the Internet is overflowing with music score resources. Having so many music score resources available, professional learners and amateur music lovers are unable to identify and obtain music score information that matches their needs and wasting valuable time. Due to the rapid development of deep learning algorithms, some individuals utilize these algorithms to detect piano scores and construct composition systems, reducing the need of traditional machine learning algorithms on manual design and music knowledge guidelines. This paper uses the deep learning algorithm to construct piano score recognition framework based on K-Nearest Neighbor (KNN) algorithm and formulates the recognition system into multinote that significantly improves the recognition rate for the system. The self-attention mechanism is then introduced in order to build a composition system based on a deep learning algorithm in which composition training and processes are described. Finally, a comparative experiment is conducted to evaluate the recognition accuracy for the KNN-based piano score recognition system. The results show that highest recognition accuracy of this system is 67.5%. The effect of composition system is evaluated based on prediction accuracy of notes. Three experiments are conducted to train the composition notes. As a result, the prediction accuracy of experiments 1, 2, and 3 is 89.2%, 91.8%, and 92.7%, respectively, indicating that the system has a high prediction accuracy and a perfect composition effect.

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Section: Build Composing Models Context Information Can Affectmentioning

confidence: 99%

A Deep Learning-Enabled Composition System Based on Piano Score Recognition

Shang

2022

Mobile Information Systems

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“…In recent years, with the rapid development of computer technology and the Internet, the information and data in the network is exploding, among which audio information occupies a large part of the data volume in the Internet and still shows a rising trend year by year, the most proportion of audio information is also the most important information is music information [1][2]. The rapid growth of music information provides convenience to people's life, and how to quickly and accurately retrieve the required information from the huge amount of song resources also has an important practical value [3][4].…”

Section: Introductionmentioning

confidence: 99%

The Design of a Music Scene Recognition Method and System Based on Artificial Intelligence

2023

IJAID

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The main melodic information of music can be used for content-based audio retrieval, management as features of audio files and to meet the business needs of specific departments and scenarios. The purpose of this paper is to study music scene recognition methods and system design based on artificial intelligence. The overall system architecture design is integrated by analysing the current stage of music scene recognition, proposing a modelling approach for music scene recognition system for music scene recognition needs and a CNN-based music scene recognition approach. A music recognition module was implemented based on the music scene recognition framework, and the generic scene text recognition system was tested. The experimental results show that the average time consumed for model computational efficiency is 60ms and the cpu resource consumption is maintained at about 28%.

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