Improving the efficiency of spectral features extraction by structuring the audio files

The extraction of spectral features from a music clip is a computationally expensive task. As in order to extract accurate features, we need to process the clip for its whole length. This preprocessing task creates a large overhead and also makes the extraction process slower. We show how formatting a dataset in a certain way, can help make the process more efficient by eliminating the need for processing the clip for its whole duration, and still extract the features accurately. In addition, we discuss the possibility of defining set generic durations for analyzing a certain type of music clip while training. And in doing so we cut down the need of processing the clip duration to just 10% of the global average

[[alternative]]Content-Based Music Retrieval

計畫編號：NSC92-2213-E032-021研究期間：200308~200407研究經費：471,000[[sponsorship]]行政院國家科學委員

The dangers of parsimony in query-by-humming applications

Query-by-humming systems attempt to address the needs of the non-expert user, for whom the most natural query format -- for the purposes of finding a tune, hook or melody of unknown providence -- is to sing it. While human listeners are quite tolerant of error in these queries, a music retrieval mechanism must explicitly model such errors in order to perform its task. We will present a unifying view of existing models, illuminating the assumptions underlying their respective designs, and demonstrating where such assumptions succeed and fail, through analysis and real-world experiments

A Comprehensive Trainable Error Model for Sung Music Queries

We propose a model for errors in sung queries, a variant of the hidden Markov model (HMM). This is a solution to the problem of identifying the degree of similarity between a (typically error-laden) sung query and a potential target in a database of musical works, an important problem in the field of music information retrieval. Similarity metrics are a critical component of query-by-humming (QBH) applications which search audio and multimedia databases for strong matches to oral queries. Our model comprehensively expresses the types of error or variation between target and query: cumulative and non-cumulative local errors, transposition, tempo and tempo changes, insertions, deletions and modulation. The model is not only expressive, but automatically trainable, or able to learn and generalize from query examples. We present results of simulations, designed to assess the discriminatory potential of the model, and tests with real sung queries, to demonstrate relevance to real-world applications

Automatic Thematic Extractor

We have created a system that identifies musical “keywords” or themes. The system searches for all patterns composed of melodic (intervallic for our purposes) repetition in a piece. This process generally uncovers a large number of patterns, many of which are either uninteresting or only superficially important. Filters reduce the number or prevalence, or both, of such patterns. Patterns are then rated according to perceptually significant characteristics. The top-ranked patterns correspond to important thematic or motivic musical content, as has been verified by comparisons with published musical thematic catalogs. The system operates robustly across a broad range of styles, and relies on no meta-data on its input, allowing it to independently and efficiently catalog multimedia data.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46483/1/10844_2004_Article_5122823.pd

Text mining techniques for patent analysis.

Abstract Patent documents contain important research results. However, they are lengthy and rich in technical terminology such that it takes a lot of human efforts for analyses. Automatic tools for assisting patent engineers or decision makers in patent analysis are in great demand. This paper describes a series of text mining techniques that conforms to the analytical process used by patent analysts. These techniques include text segmentation, summary extraction, feature selection, term association, cluster generation, topic identification, and information mapping. The issues of efficiency and effectiveness are considered in the design of these techniques. Some important features of the proposed methodology include a rigorous approach to verify the usefulness of segment extracts as the document surrogates, a corpus-and dictionary-free algorithm for keyphrase extraction, an efficient co-word analysis method that can be applied to large volume of patents, and an automatic procedure to create generic cluster titles for ease of result interpretation. Evaluation of these techniques was conducted. The results confirm that the machine-generated summaries do preserve more important content words than some other sections for classification. To demonstrate the feasibility, the proposed methodology was applied to a realworld patent set for domain analysis and mapping, which shows that our approach is more effective than existing classification systems. The attempt in this paper to automate the whole process not only helps create final patent maps for topic analyses, but also facilitates or improves other patent analysis tasks such as patent classification, organization, knowledge sharing, and prior art searches

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