Music Information Retrieval using an Optimal Time Series Matching

The enormous growth of digital music industry during the past decades requires retrieval of music from enormous amount of music data. It is the second most popular form of multimedia retrieval. Music is considered as an art form consisting of sequences of sound in time. Music is a specific kind of sound conveying massive meaningful information. In this paper, the acoustic features are extracted and its aligned by making use of dynamic similarity matching method such as Dynamic Time Warping and the score for exact match is calculated using Longest Common Subsequence algorithm their minimum cost for matching is analyzed. Especially it finds the longest subsequence that two sequences have in common, regardless of the length and the number of intermittent non-matching symbols. Finally the performance analysis is computed by means of extracted audio features. I.INTRODUCTION The tremendous growth of the Internet, information exchange and fast access to information has become the requirements of the day. Search engines like Google, Alta-vista, Yahoo etc search from huge databases and retrieve textual content ( and to some extent images ) pretty fast. Textual content retrieval has become more or less trivial with the presence of these search engines. Recently, Google-Desktop has made powerful search functionalities possible on personal computers. With the ever increasing presence of various forms of multimedia such as images, audio and video on the web and on PCs, content based multimedia retrieval is seen as a necessity. The second most popular form of multimedia is audio. Many current search engines in websites that distribute audio on the web are textual meta-label based, i.e search can only be done in the form of predefined and restricted queries such as search an audio by singer or album name. Over a billion songs on the web, tasks such as indexing and labelling are still done manually. This leads to incompatibilities among various websites. Some applications (e.g, Musipedia, soundhound, tunefind,etc.) employ content-based method to search music relying on melody, rhythm, timbre, harmony, which provide advanced search options different music contents. But music itself is the expression of emotions conveying meaningful information, which can be highly subjective and difficult to quantify. Though automatic recognition of emotions in music is still in its early stages it has received increasing attention in recent years. Determining the emotional content of music by mathematical calculation is a cross-disciplinary try spanning not only signal processing and machine learning, but also requiring an understanding of auditory perception, psychology, and music theory. There have been several computational approaches proposed to determine the similarity between the songs, however the techniques work only on few songs and also perform poorly in terms of scalability. II.METHODS FOR RETRIEVAL Various features can be extracted from an audio file, usually three basic strategies are applied to solve various scenarios in music information retrieval. The first strategy is based on the metadata information, most of commercial music service providers utilized metadata information such as music title, genre, album, lyrics, and biography to search [2] , formulating a form of query-by-text [4] music retrieval . But these metadata information to index and retrieve music that the user want to find . This leads to incompatibilities among various websites . Music contents contains a variety of International Conference on New Frontiers of Engineering, Science, Management and Humanities (ICNFESMH-2017) at (NITTTR) National Institute of Technical Teachers Training & Research, Chandigarh, India (MHRD, Govt. of India) on 21 May 2017 ISBN: 978-81-934083-0-8 31 G.S. Mathumitha, Dr. S.Sridhar music attributes (e.g., artist, genre, pitch, intensity, timbre, rhythm, harmony) and low-level acousticdescriptors (e.g., spectrum, MFCCs) [3] . Consequently, many researchers carry on music retrieval based on content-based method concentrating on the features of melody, rhythm, timbre, harmony, and MFCCs [5]. Music content is represented by acoustic features, and the acoustic similarity between two pieces of music is computed to carry on music retrieval. In a probabilistic model that combines tags, acoustic similarity and context based retrieval and recommendation was carried out in music. Recently many research work focus on semantic retrieval of music . Semantic-based music retrieval utilizes the high-level semantic description of music . In order to obtain semantic descriptors, music has to be annotated by meaningful words. Different methods of music annotation can be found in , in which a generic audio music will be annotated with semantically meaningful words usually using a supervised multiclass labeling paradigm. The aim of emotion-based music retrieval is to retrieve music at the emotional level. The current work is primarily motivated by James et al[1] where resonance, arousal and valance based on time varying emotion feature are extracted. Although there are less research works directly addressed the problem of emotion-based music retrieval, many related works such as music emotion classification or recognition still can be referenced. In audio matching the goal is to automatically retrieve all passages (subsegments) from all audio documents that musically correspond to a given query excerpt. Of course, the two scenarios seamlessly merge into each other. Fortunately, there are still an increasing number of studies on time series analysis of music and emotion . For example, Serr et al [9]use a local matching strategy for global document retrieval like exact string matching where a discrete equivalent to a matched filter is an exact string comparison, while the string comparison is exact, the result is an approximate match because of quantization. When quantizing a low dimensional signal we can talk about neighbouring symbols and we expect some errors are more acceptable than others. But in high dimensions, the curse of dimensionality means that all errors are similar. Because of the small length of music excerpts we are using, we do not expect that exact-string matching to produce very robust results: false positives will be higher. Another approach ,edit distance matching uses vector quantization (VQ) has been successfully employed in a wide range of applications in automatic speech recognition and music information retrieval. Chroma features are collapsed into string sequences using vector quantization (VQ) and best retrieval is achieved by calculating the string-edit distance between these sequences. Success is demonstrated for finding repeated patterns within a song. But they lack in interpretability of the VQ-produced strings encourages the use of metrics that consider all character swaps to be the same. In dynamic programming local alignment the Smith-Waterman algorithm [9] is a well-known algorithm for performing local sequence alignment in Molecular Biology. It was originally designed for determining similar regions between two protein sequences. Instead of looking at the total sequence, the SmithWaterman algorithm compares segments of all possible lengths and optimizes the similarity measure. However, the Smith-Waterman local alignment has low tolerance to continuous errors. Finally a direct matching method poses high matching precision may be usually obtained in this way but at the cost of increased computational complexity. Variations of tempo in the sung query make the standard Euclidean distance between vectors inaccurate and a more sophisticated matching algorithm must be applied. The method of choice for aligning the query with a template via a non-linear scaling of the time domain is known as Dynamic Time Warping (DTW). For our experiment, we have chosen 50 classical music excerpts from RWC music database . The stimulus materials consist of fifty classical music excerpts. The styles of these music excerpts contain classical music include Bach, Beethoven, Chopin, Mozart, Schubert,etc are given as input clip.All music audio excerpts are converted to the uniform format, with sampling rate 16000 Hz, 16 bits, 512bit rate and stereo channel. Each experimental music clip is truncated to 12 to 15 seconds length to avoid a long piece of music associated with multiple emotions. III RETRIEVAL SYSTEM Time series analysis has provided a powerful way of mining data over time during the past decades. In this paper similarity between two excerpts are investigated using warping strategies. Here the Figure 3.1 shows an overview of proposed music retrieval system. International Conference on New Frontiers of Engineering, Science, Management and Humanities (ICNFESMH-2017) at (NITTTR) National Institute of Technical Teachers Training & Research, Chandigarh, India (MHRD, Govt. of India) on 21 May 2017 ISBN: 978-81-934083-0-8 32 G.S. Mathumitha, Dr. S.Sridhar Figure 3.1 System Architecture. The aim of feature extraction is to extract a set of representative features to simplify analysis of raw musical audio data. Time-domain and frequency domain audio features are selected.