Content-based music retrieval by acoustic query

Ph.DDOCTOR OF PHILOSOPH

Search Engine for WAV files

This project deals with matter pertaining to the application of search engine for WAV files. The objective of this project is to create an application that is capable of searching for WAVfiles by comparing the criteria of the input and the WAVfiles in the database. The emphasisis placed on the aims at providing user with the technologythat will help them to analyze while comparing the differences between the input given and the original WAV file in the database. This application is meant to assist user that have problem searching for the matching title of a particularWAVfiles. The main features of the application are that I) it receives any kind of input form from the microphone, 2) it then compares and matchesthe criteria ofWAVfiles from triedatabase using specified method. The scope of study is the process of how audio recognition work and how it could be compared based on certain identified criteria to find the matches. It basically focuses and emphasizes on WAV file format comparison but in a broaderperspective of research in WAV file criteria and attributes. I don't have any specified framework in doingmy project, yet I follow iterative guidelines where muchof the processes are done in parallel and without planning. The finding will be focusing on the issues on how to capture, compare and match WAV files format Basically, by using the underlying concepts the project will be able to compare the inputWAV format and match with the original intended song easily and faster

Content-based retrieval of melodies using artificial neural networks

Human listeners are capable of spontaneously organizing and remembering a continuous stream of musical notes. A listener automatically segments a melody into phrases, from which an entire melody may be learnt and later recognized. This ability makes human listeners ideal for the task of retrieving melodies by content. This research introduces two neural networks, known as SONNETMAP and _ReTREEve, which attempt to model this behaviour. SONNET-MAP functions as a melody segmenter, whereas ReTREEve is specialized towards content-based retrieval (CBR). Typically, CBR systems represent melodies as strings of symbols drawn from a finite alphabet, thereby reducing the retrieval process to the task of approximate string matching. SONNET-MAP and ReTREEwe, which are derived from Nigrin’s SONNET architecture, offer a novel approach to these traditional systems, and indeed CBR in general. Based on melodic grouping cues, SONNETMAP segments a melody into phrases. Parallel SONNET modules form independent, sub-symbolic representations of the pitch and rhythm dimensions of each phrase. These representations are then bound using associative maps, forming a two-dimensional representation of each phrase. This organizational scheme enables SONNET-MAP to segment melodies into phrases using both the pitch and rhythm features of each melody. The boundary points formed by these melodic phrase segments are then utilized to populate the iieTREEve network. ReTREEw is organized in the same parallel fashion as SONNET-MAP. However, in addition, melodic phrases are aggregated by an additional layer; thus forming a two-dimensional, hierarchical memory structure of each entire melody. Melody retrieval is accomplished by matching input queries, whether perfect (for example, a fragment from the original melody) or imperfect (for example, a fragment derived from humming), against learned phrases and phrase sequence templates. Using a sample of fifty melodies composed by The Beatles , results show th a t the use of both pitch and rhythm during the retrieval process significantly improves retrieval results over networks that only use either pitch o r rhythm. Additionally, queries that are aligned along phrase boundaries are retrieved using significantly fewer notes than those that are not, thus indicating the importance of a human-based approach to melody segmentation. Moreover, depending on query degradation, different melodic features prove more adept at retrieval than others. The experiments presented in this thesis represent the largest empirical test of SONNET-based networks ever performed. As far as we are aware, the combined SONNET-MAP and -ReTREEue networks constitute the first self-organizing CBR system capable of automatic segmentation and retrieval of melodies using various features of pitch and rhythm

Search Engine for WAV files

This project deals with matter pertaining to the application of search engine for WAV files. The objective of this project is to create an application that is capable of searching for WAVfiles by comparing the criteria of the input and the WAVfiles in the database. The emphasisis placed on the aims at providing user with the technologythat will help them to analyze while comparing the differences between the input given and the original WAV file in the database. This application is meant to assist user that have problem searching for the matching title of a particularWAVfiles. The main features of the application are that I) it receives any kind of input form from the microphone, 2) it then compares and matchesthe criteria ofWAVfiles from triedatabase using specified method. The scope of study is the process of how audio recognition work and how it could be compared based on certain identified criteria to find the matches. It basically focuses and emphasizes on WAV file format comparison but in a broaderperspective of research in WAV file criteria and attributes. I don't have any specified framework in doingmy project, yet I follow iterative guidelines where muchof the processes are done in parallel and without planning. The finding will be focusing on the issues on how to capture, compare and match WAV files format Basically, by using the underlying concepts the project will be able to compare the inputWAV format and match with the original intended song easily and faster