scholarly journals Songs Recognition Using Audio Information Fusion

2015 ◽  
Vol 61 (1) ◽  
pp. 37-41
Author(s):  
Paweł Biernacki
Keyword(s):  
Information Fusion ◽  
Audio Signal ◽  
Relevant Information ◽  
Correct Identification ◽  
Acoustic Features ◽  
Signal Features ◽  
Information Fusion Technique ◽  
Music Audio ◽  
Audio Information ◽  
Fusion Approach

Abstract The article presents information fusion approach for song classification with use of acoustic signal. Many acoustic features can contribute to correct identification of a song. Taking into consideration only one set of features may result in omission of relevant information. It is possible to improve the accuracy of identification process by means of the information fusion technique, in which various aspects of acoustic fingerprint are taken into consideration. Two sets of signal features were distinguished: one were based on frequency analysis (harmonic elements) and the other were based on multidimensional correlation ratios. An identification of a commercial was made with use of SVM and k-NN classifiers. The music audio signal database was used for assessing the effectiveness of the proposed solution. Results show an improved effectiveness of identification in relation to applying only one set of song features

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.

scholarly journals Inference of Mood State Indices by Using a Multimodal High-Level Information Fusion Technique

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chih-Hua Tai ◽  
Kuo-Hsuan Chung ◽  
Ya-Wen Teng ◽  
Feng-Ming Shu ◽  
Yue-Shan Chang
Keyword(s):  
Information Fusion ◽  
Mood State ◽  
Fusion Technique ◽  
Level Information ◽  
Information Fusion Technique ◽  
High Level

scholarly journals Mine ventilator fault diagnosis based on information fusion technique

2009 ◽  
Vol 1 (1) ◽  
pp. 1484-1488 ◽  
Author(s):  
Shi Li-ping ◽  
Han Li ◽  
Wang Ke-wu ◽  
Zhang Chuan-juan
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Fusion Technique ◽  
Information Fusion Technique

scholarly journals DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks

2020 ◽  
Author(s):  
Pablo Rodríguez-Mier ◽  
Nathalie Poupin ◽  
Carlo de Blasio ◽  
Laurent Le Cam ◽  
Fabien Jourdan
Keyword(s):  
Metabolic Network ◽  
Metabolic Networks ◽  
Relevant Information ◽  
Experimental Information ◽  
Correct Identification ◽  
Good Representation ◽  
Metabolic State ◽  
Whole Space ◽  
Context Specific ◽  
Optimal Networks

AbstractThe correct identification of metabolic activity in tissues or cells under different environmental or genetic conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific organism and condition, containing only the reactions predicted to be active in such context. A major limitation of this approach is that the available information does not generally allow for an unambiguous characterization of the corresponding optimal metabolic sub-network, i.e., there are usually many different sub-network that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic state. In this work, we formalize the problem of enumeration of optimal metabolic networks, we implement a set of techniques that can be used to enumerate optimal networks, and we introduce DEXOM, a novel strategy for diversity-based extraction of optimal metabolic networks. Instead of enumerating the whole space of optimal metabolic networks, which can be computationally intractable, DEXOM samples solutions from the set of optimal metabolic sub-networks maximizing diversity in order to obtain a good representation of the possible metabolic state. We evaluate the solution diversity of the different techniques using simulated and real datasets, and we show how this method can be used to improve in-silico gene essentiality predictions in Saccharomyces Cerevisiae using diversity-based metabolic network ensembles. Both the code and the data used for this research are publicly available on GitHub1.

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