Cultural style based music classification of audio signals

Liu, Yuxiang; Xiang, Qiao-Liang; Wang, Ye; Cai, Lianhong

doi:10.1109/icassp.2009.4959519

Cited by 12 publications

(8 citation statements)

References 4 publications

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“…Mel scale relates the perceived frequency and actual frequency with relationship given in (7). The forty band pass filters (which spans the entire audible range) have triangular frequency response with 50 % overlap, and the centre frequencies are linearly spaced on the Mel scale as shown in fig.1.…”

Section: ) Mfccmentioning

confidence: 99%

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Extraction of feature vectors for analysis of musical instruments

Joshi¹,

Nadgir²

2014

2014 International Conference on Advances in Electronics Computers and Communications

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Section: ) Mfccmentioning

confidence: 99%

“…1) Spectral Flux: Spectral flux is the distance between the spectra of each successive frame and is related to the variation of spectrum over time [6], [7]. Spectral flux is calculated using (3).…”

Section: B Features In Spectral Domainmentioning

confidence: 99%

Extraction of feature vectors for analysis of musical instruments

Joshi¹,

Nadgir²

2014

2014 International Conference on Advances in Electronics Computers and Communications

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“…• Decision Tree (DT) [15,19]: As a logic-based algorithm, DT models data sets in hierarchical structures using a series of if/else statement comparisons. Each node in the tree is made up of either decision nodes that contain terms (or objects that are more complex) or leaves that contain class label predictions.…”

Section: Algorithms Of ML For Chmentioning

confidence: 99%

“…• KNN (K-Nearest Neighbors) [6,19,21,22,30]: this is a statistical method for predicting new input by calculating the similarity between the test data and the new instance by locating the closest data points (or data objects) in the training dataset based on certain distance functions. K denotes the number of closest data points (i.e., neighbors).…”

Section: Algorithms Of ML For Chmentioning

confidence: 99%

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An Approach based on Machine Learning Algorithms for the Recommendation of Scientific Cultural Heritage Objects

Nafis¹,

Fararni²,

Yahyaouy³

et al. 2021

IJACSA

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The Scientific Cultural Heritage (SCH) of the Drâa-Tafilalet region in south-eastern Morocco is a rich source of data testifying to the ingenuity of an older generation that has shaped the past of the region. These data must be preserved for future generations, particularly with new technologies and the semantic web. Recommendation systems (RS) are intended to assist prospective users in recommending the most suitable services based on their profile and expectations. The collaborative filtering (CF), content filtering (CB) or hybrid filtering (CF) RS has shown promising results in order to explore the problems experienced especially in CH. However, there are some limitations to be resolved, mostly due to the ability of these methods to build a stable and complete framework, which can provide a complete image of the user profile and suggest the most appropriate offers. This paper presents a hybrid recommender system for SCH data; a field little explored despite its historical importance and the value it generates. The results presented in this paper belong to the data collected from the region of Drâa-Tafilalet in southern Morocco.

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A Survey on Artificial Intelligence-Based Acoustic Source Identification

Zaheer,

Ahmad,

Habibi

et al. 2023

IEEE Access

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The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions.

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Cultural style based music classification of audio signals

Cited by 12 publications

References 4 publications

Extraction of feature vectors for analysis of musical instruments

Extraction of feature vectors for analysis of musical instruments

An Approach based on Machine Learning Algorithms for the Recommendation of Scientific Cultural Heritage Objects

A Survey on Artificial Intelligence-Based Acoustic Source Identification

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