Content-based music structure analysis with applications to music semantics understanding (original) (raw)

Automatic structure detection for popular music

IEEE Multimedia, 2006

Music structure is very important for semantic music understanding. We propose a novel approach for popular music structure detection. The proposed approach applies beat space segmentation, chord detection, singing voice boundary detection, melody and content based similarity region detection to music structure detection. A frequency scaling "Octave Scale" is used to calculate Cepstral coefficients to represent the music content. The experiments illustrate that the proposed approach achieves better performance than existing methods. We also outline some applications which can use our refined music structural analysis. Rest Semibreve Minim Crotchet Quaver Semiquaver Demisemiquaver Note Shape Corresponding names generally used in U.

AUDIO-BASED MUSIC STRUCTURE ANALYSIS

Humans tend to organize perceived information into hierarchies and structures, a principle that also applies to music. Even musically untrained listeners unconsciously analyze and segment music with regard to various musical aspects, for example, identifying recurrent themes or detecting temporal boundaries between contrasting musical parts. This paper gives an overview of state-of-the-art methods for computational music structure analysis, where the general goal is to divide an audio recording into temporal segments corresponding to musical parts and to group these segments into musically meaningful categories. There are many different criteria for segmenting and structuring music audio. In particular, one can identify three conceptually different approaches, which we refer to as repetition-based, novelty-based, and homogeneity-based approaches. Furthermore, one has to account for different musical dimensions such as melody, harmony, rhythm, and timbre. In our state-of-the-art report, we address these different issues in the context of music structure analysis, while discussing and categorizing the most relevant and recent articles in this field.

Music structure analysis by finding repeated parts

Proceedings of the 1st ACM workshop on Audio and music computing multimedia - AMCMM '06, 2006

The structure of a musical piece can be described with segments having a certain time range and a label. Segments having the same label are considered as occurrences of a certain structural part. Here, a system for finding structural descriptions is presented. The problem is formulated in terms of a cost function for structural descriptions. A method for creating multiple candidate descriptions from acoustic input signal is presented, and an efficient algorithm is presented to find the optimal description with regard to the cost function from the candidate set. The analysis system is evaluated with simulations on a database of 50 popular music pieces.

Automatic Music Summarization Based on Music Structure Analysis

Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.

In this paper, we present a novel approach for music summarization based on music structure analysis. From the audio signal, we first extract the note onset representing the time tempo of the song and the music structure analysis can be performed based on this tempo information. After music content has been structured into different semantic regions such as Introduction (Intro), Verse, Chorus, Ending (Outro), etc., the final music summary can be created with chorus and music phrases which are included anterior or posterior to selected chorus to get the desired length of the final summary. In this way, we can guarantee that the summaries begin and end at meaningful music phrase boundaries, which is a difficult problem for existing music summarization methods. Experiments show our proposed method can capture the main theme of the music compared to the ideal summaries selected by music experts and user subjective evaluation indicates our proposed method has a good performance.

Music Structure Segmentation Algorithm Evaluation: Expanding on MIREX 2010 Analyses and Datasets

2011

ABSTRACT Music audio structure segmentation has been a task in the Music Information Retrieval Evaluation eXchange (MIREX) since 2009. In 2010, five algorithms were evaluated against two datasets (297 and 100 songs) with an almost exclusive focus on western popular music. A new annotated dataset significantly larger in size and with a more diverse range of musical styles became available in 2011. This new dataset comprises over 1,300 songs spanning pop, jazz, classical, and world music styles.

Music structure based vector space retrieval

Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '06, 2006

This paper proposes a novel framework for music content indexing and retrieval. The music structure information, i.e., timing, harmony and music region content, is represented by the layers of the music structure pyramid. We begin by extracting this layered structure information. We analyze the rhythm of the music and then segment the signal proportional to the inter-beat intervals. Thus, the timing information is incorporated in the segmentation process, which we call Beat Space Segmentation.

Computational Music Structure Analysis

Music is a ubiquitous and vital part of the lives of billions of people worldwide. Musical creations and performances are among the most complex and intricate of our cultural artifacts, and the emotional power of music can touch us in surprising and profound ways. In view of the rapid and sustained growth of digital music sharing and distribution, the development of computational methods to help users find and organize music information has become an important field of research in both industry and academia. The Dagstuhl Seminar 16092 was devoted to a research area known as music structure analysis, where the general objective is to uncover patterns and relationships that govern the organization of notes, events, and sounds in music. Gathering researchers from different fields, we critically reviewed the state of the art for computational approaches to music structure analysis in order to identify the main limitations of existing methodologies. This triggered interdisciplinary discussions that leveraged insights from fields as disparate as psychology, music theory, composition, signal processing, machine learning, and information sciences to address the specific challenges of understanding structural information in music. Finally, we explored novel applications of these technologies in music and multimedia retrieval, content creation, musicology, education, and human-computer interaction. In this report, we give an overview of the various contributions and results of the seminar. We start with an executive summary, which describes the main topics, goals, and group activities. Then, we present a list of abstracts giving a more detailed overview of the participants' contributions as well as of the ideas and results discussed in the group meetings of our seminar. Seminar February 28–March 4, 2016 – http://www.dagstuhl.de/16092 1998 ACM Subject Classification H.5.5 Sound and Music Computing In this executive summary, we start with a short introduction to computational music structure analysis and then summarize the main topics and questions raised in this seminar.

New methods in structural segmentation of musical audio

2006

We describe a simple model of musical structure and two related methods of extracting a high-level segmentation of a music track from the audio data, including a novel use of hidden semi-Markov models. We introduce a semi-supervised segmentation process which finds musical structure with improved accuracy given some very limited manual input. We give experimental results compared to existing methods and human segmentations.

An Integrated Approach to Music Boundary Detection

2009

Music boundary detection is a fundamental step of music analysis and summarization. Existing works use either unsupervised or supervised methodologies to detect boundary. In this paper, we propose an integrated approach that takes advantage of both methodologies. In particular, a graph-theoretic approach is proposed to fuse the results of an unsupervised model and a supervised one by the knowledge of the typical length of a music section. To further improve accuracy, a number of novel mid-level features are developed and incorporated to the boundary detection framework. Evaluation result on the RWC dataset shows the effectiveness of the proposed approach.

Automatic Detection of the Musical Structure within Pieces of Music Masters

2011

Music Structure Discovery (MSD) for popular music is a well known task in Music Information Retrieval (MIR). In this thesis a new approach for finding the musical structure of a piece of music is proposed. The algorithm is based on the search for repeated vertical slices inside a modified bar level Self Distance Matrix (SDM) using a template matching algorithm. After an initial segmentation is found based on the analysis of the template matching results, a post processing step helps to further investigate the found musical structure by searching for repeated sub-sequences in the preliminary segmentation.