Pencarian Melodi pada File Midi

Searching MIDI files is generally done by looking at their file names or the metadata/tags inside them. Searching for a certain melody in a MIDI file must be done by looking at the contents on the MIDI file. When searching for a melody in a MIDI file, the user inputs a melody as a query. The system will then search for MIDI files that contain the most similar melodies compared to the query. The queried melody is monophonic while melodies contained in MIDI files are polyphonic. Therefore, melodies in the MIDI files must be converted to monophonic in order to enable comparison. The comparison of two monophonic melodies is done by calculating their dissimilarity factor. The smaller the dissimilarity factor, the more similar the two melodies are. A zero dissimilarity factor means the two melodies are identical. There are two comparison methods used, i.e. bar per bar comparison and note per note comparison. Bar per bar comparison is faster but is very fussy about the position of the melody in the bar. Note per note comparison is more accurate but takes longer to search

Streaming Audio Using MPEG–7 Audio Spectrum Envelope to Enable Self-similarity within Polyphonic Audio

One method overlooked to date, which can work alongside existing audio compression schemes, is that which takes account of the semantics and natural repetition of music through meta-data tagging. Similarity detection within polyphonic audio has presented problematic challenges within the field of Music Information Retrieval.  This paper presents a method (SoFI) for improving the quality of stored audio being broadcast over any wireless medium through meta-data which has a number of market applications all with market value. Our system works at the content level thus rendering it applicable in existing streaming services. Using the MPEG-7 Audio Spectrum Envelope (ASE) gives features for extraction and combined with k-means clustering enables self-similarity to be performed within polyphonic audio. SoFI uses string matching to identify similarity between large sections of clustered audio. Objective evaluations of SoFI give positive results which show that SoFI is shown to detect high levels of similarity on varying lengths of time within an audio file. In a scale between 0 and 1 with 0 the best, a clear correlation between similarly identified sections of 0.2491 shows successful identification

Pattern Matching Techniques for Replacing Missing Sections of Audio Streamed across Wireless Networks

Streaming media on the Internet can be unreliable. Services such as audio-on-demand drastically increase the loads on networks; therefore, new, robust, and highly efficient coding algorithms are necessary. One method overlooked to date, which can work alongside existing audio compression schemes, is that which takes into account the semantics and natural repetition of music. Similarity detection within polyphonic audio has presented problematic challenges within the field of music information retrieval. One approach to deal with bursty errors is to use self-similarity to replace missing segments. Many existing systems exist based on packet loss and replacement on a network level, but none attempt repairs of large dropouts of 5 seconds or more. Music exhibits standard structures that can be used as a forward error correction (FEC) mechanism. FEC is an area that addresses the issue of packet loss with the onus of repair placed as much as possible on the listener's device. We have developed a server--client-based framework (SoFI) for automatic detection and replacement of large packet losses on wireless networks when receiving time-dependent streamed audio. Whenever dropouts occur, SoFI swaps audio presented to the listener between a live stream and previous sections of the audio stored locally. Objective and subjective evaluations of SoFI where subjects were presented with other simulated approaches to audio repair together with simulations of replacements including varying lengths of time in the repair give positive results.</jats:p

Rhythmic analysis of motion signals for music retrieval

viii, 108 leaves : ill. (chiefly col.) ; 29 cm.Includes abstract and appendix.Includes bibliographical references (leaves 100-108).This thesis presents a framework that queries a music database with rhythmic motion signals. Rather than the existing method to extract the motion signal's underlying rhythm by marking salient frames, this thesis proposes a novel approach, which converts the rhythmic motion signal to MIDI-format music and extracts its beat sequence as the rhythmic information of that motion. We extract "motion events" from the motion data based on characteristics such as movement directional change, root-y coordinate and angular-velocity. Those events are converted to music notes in order to generate an audio representation of the motion. Both this motion-generated music and the existing audio library are analyzed by a beat tracking algorithm. The music retrieval is completed based on the extracted beat sequences. We tried three approaches to retrieve music using motion queries, which are a mutual-information-based approach, two sample KS test and a rhythmic comparison algorithm. Feasibility of the framework is evaluated with pre-recorded music and motion recordings

A Polyphonic Music Retrieval System Using N-Grams

This paper describes the development of a polyphonic music retrieval system with the n-gram approach. Musical n-grams are constructed from polyphonic musical performances in MIDI using the pitch and rhythm dimensions of music. These are encoded using text characters enabling the musical words generated to be indexed with existing text search engines. The Lemur Toolkit was adapted for the development of a demonstrator system on a collection of around 10,000 polyphonic MIDI performances. The indexing, search and retrieval with musical n-grams and this toolkit have been extensively evaluated through a series of experimental work over the past three years, published elsewhere. We discuss how the system works internally and describe our proposal for enhancements to Lemur towards the indexing of ‘overlaying ’ as opposed to indexing a ‘bag of terms’. This includes enhancements to the parser for a ‘polyphonic musical word indexer ’ to incorporate within document position information when indexing adjacent and concurrent musical words. For retrieval of these ‘overlaying ’ musical words, a new proximity-based operator and a ranking function is proposed. 1