Audio Information Retrieval and Musical Acoustics

2021 ◽  
Vol 24 (7) ◽  
pp. 10-20
Author(s):  
Marco Olivieri ◽  
Raffaele Malvermi ◽  
Mirco Pezzoli ◽  
Massimiliano Zanoni ◽  
Sebastian Gonzalez ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Musical Acoustics ◽  
Audio Information

scholarly journals 402 Audio information retrieval for describing gait patterns in Brazilian horses

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 27-27
Author(s):  
Ricardo V Ventura ◽  
Rafael Z Lopes ◽  
Lucas T Andrietta ◽  
Fernando Bussiman ◽  
Julio Balieiro ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Subjective Evaluation ◽  
Audio Signal ◽  
Principal Component ◽  
Potential Method ◽  
Economic Sectors ◽  
Audio Features ◽  
Horse Industry ◽  
Audio Files ◽  
Audio Information

Abstract The Brazilian gaited horse industry is growing steadily, even after a recession period that affected different economic sectors in the whole country. Recent numbers suggested an increase on the exports, which reveals the relevance of this horse market segment. Horses are classified according to the gait criteria, which divide the horses in two groups associated with the animal movements: lateral (Marcha Picada) or diagonal (Marcha_Batida). These two gait groups usually show remarkable differences related to speed and number of steps per fixed unit of time, among other factors. Audio retrieval refers to the process of information extraction obtained from audio signals. This new data analysis area, in comparison to traditional methods to evaluate and classify gait types (as, for example, human subjective evaluation and video monitoring), provides a potential method to collect phenotypes in a reduced cost manner. Audio files (n = 80) were obtained after extracting audio features from freely available YouTube videos. Videos were manually labeled according to the two gait groups (Marcha Picada or Marcha Batida) and thirty animals were used after a quality control filter step. This study aimed to investigate different metrics associated with audio signal processing, in order to first cluster animals according to the gait type and subsequently include additional traits that could be useful to improve accuracy during the identification of genetically superior animals. Twenty-eight metrics, based on frequency or physical audio aspects, were carried out individually or in groups of relative importance to perform Principal Component Analysis (PCA), as well as to describe the two gait types. The PCA results indicated that over 87% of the animals were correctly clustered. Challenges regarding environmental interferences and noises must be further investigated. These first findings suggest that audio information retrieval could potentially be implemented in animal breeding programs, aiming to improve horse gait.

An Information Retrieval System Based on Portable Device

2013 ◽  
Vol 712-715 ◽  
pp. 2706-2711
Author(s):  
Xiao Qing Yu ◽  
Wen Gen Wang ◽  
Jian Hua Shi ◽  
Yun Hui Wang
Keyword(s):  
Information Retrieval ◽  
Retrieval System ◽  
Relevant Information ◽  
Information Retrieval System ◽  
Text Indexing ◽  
Traditional System ◽  
Related Information ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Audio Information

Information retrieval is the activity to organize information in a certain way, and according to the users demand to find out the related information from a collection of resources. Retrieval process and technology can be based on metadata or full-text indexing. Most of the relevant information retrieval systems are devised on the computer. However, with the highly development of the embedded technology, some popular application have been developed on the platform. In this paper, we will introduce an information retrieval system on the iOS platform which is more convenient, practical, and effective compared with the traditional system. And we will introduce an application based on this system design. The experiments shown that this system was exactly effective utilized to retrieval audio information.

Concept framework for audio information retrieval: ARF

2003 ◽  
Vol 18 (5) ◽  
pp. 667-673 ◽  
Author(s):  
GuoHui Li ◽  
DeFeng Wu ◽  
Jun Zhang
Keyword(s):  
Information Retrieval ◽  
Audio Information ◽  
Concept Framework
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