Laryngeal mechanisms are physical adjustments of the vocal tract that enable the human voice capacity of producing a wide frequency range. We found that labeling for more than 2 values it is not recommended to use a bipartite ranking if there are many repetitive data, both k-partite ranking and rank by pairwise comparison are able to be used for multi-dimensional data labeling. After training dataset and evaluate the validation dataset using NDCG, it is found that the collaborative ranking used have a more accurate value / lower variance test evaluation because it uses a large dataset and smaller training dataset. We're used questionnaires and deployment of prototype of Intelligent Personal Assistant Agent to apply the appropriate algorithm in intelligence agent in arranging task priority in daily activity that must be done by the users. Our approach is to compare several algorithms performed in the process of ranking that are Bipartite Ranking, k-partite Ranking, and Ranking by pairwise comparison. This work focuses on finding the right approach and corresponding algorithms in the process of ranking to be able to help people in determining which jobs have a higher priority than others. Task ranking is one of the problems that can be solved by using a machine learning algorithm ranking problem. There are variety of methods and algorithms that can be used to overcome the ranking problem. The aim of this state-of-art paper is to produce a summary and guidelines for using the broadly used methods, to identify the challenges as well as future research directions of acoustic signal processing. The paper provides the survey of the state-of art for understanding ASC’s general research scope, including different types of audio representation of audio like acoustic, spectrogram audio feature extraction techniques like physical, perceptual, static, dynamic audio pattern matching approaches like pattern matching, acoustic phonetic, artificial intelligence classification, and clustering techniques. Based on the application’s classification domain, the characteristics extraction and classification/clustering algorithms used may be quite diverse. Audio signal classification (ASC) comprises of generating appropriate features from a sound and utilizing these features to distinguish the class the sound is most likely to fit. Audio signal processing is the most challenging field in the current era for an analysis of an audio signal.
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