Analisis dan Implementasi Short Time Fourier Transform (STFT) dan Approximate String Matching pada Query-by-Humming

ABSTRAKSI: Music Information Retrieval (MIR) merupakan sebuah bidang ilmu untuk mengambil dan mengolah informasi dari file musik, dapat berupa metadata, ataupun konten, seperti nada, melodi dan tempo. Query by Humming merupakan bidang turunan dari MIR yang mencari suatu lagu di dalam database berdasarkan konten dari musik yang berupa melodi.Query by Humming terdiri dari dua tahap utama yaitu mengubah hasil humming dan file musik pada database menjadi representasi yang sesuai (menggunakan teknik Short Time Fourier Transform (STFT)) serta membandingkan hasil humming dan file musik pada database (menggunakan teknik Approximate String Matching (ASM)). STFT mengubah hasil humming dari domain waktu-amplitudo ke dalam domain waktu-frekuensi untuk memperoleh melodi yang dinyanyikan. Sedangkan ASM membandingkan dua buah string dan menghitung banyak operasi dasar yang harus dilakukan untuk mengubah string pertama menjadi string kedua. Semakin kecil hasil dari metode ini, maka semakin mirip kedua string tersebut.Pada tugas akhir ini dilakukan penelitian untuk mengetahui ukuran jendela yang sesuai pada STFT dan pembobotan operasi dasar dari ASM terhadap Query by Humming. Akurasi ditentukan oleh keberhasilan sistem untuk menebak lagu yang dinyanyikan. Untuk menghasilkan banyak kemungkinan akurasi, jendela dirancang dalam beberapa lebar segmentasi yang berbeda dan pembobotan operasi dasar juga akan dilakukan untuk beberapa nilai yang berbeda. Dari rancangan tersebut, hasil akurasi sistem mencapai 34.62%-61.54% untuk koleksi data penyanyi berpengalaman dan 9.38%-28.13% untuk koleksi data penyanyi dengan latar belakang paduan suara saja.Kata Kunci : music information retrieval (MIR), query by humming, short time fourier transform (STFT), approximate string matching (ASM), jendela, operasi dasarABSTRACT: Music Information Retrieval (MIR) is a field to retrieve and process information from music files, can be metadata, such as title and singer, or content, such as tone, melody and tempo. Query by Humming is derived from the MIR field which is looking for a song in the database based on the content of the music as melody. Query by Humming consists of two main stages, they are change the results of humming and music files in the database into the appropriate representation (using the technique of Short Time Fourier Transform (STFT)) and compare the results of humming and music files in the database (using the technique of Approximate String Matching (ASM) ). STFT change the humming of the time-amplitude domain into time-frequency domain to obtain a sung melody. While ASM compares two strings and calculates how many basic operations that must be done to change the first string to second string. The smaller the result of this method, the more similar the two strings.This final project will determine the proper window length in STFT and weighting the basic operation of the ASM to Query by Humming. Accuracy is determined by the success of the system to guess the song is sung. To generate many possible accuracy, the window is designed in several different segmentations wide and weighted base operations will also be conducted for several different values. From the draft, the accuracy of the system reaches 34.62%-61.54% for data set collected from experience singers and 9.38%-28.13% for data set collecter from singers experienced in choir only.Keyword: music information retrieval (MIR), query by humming, short time fourier transform (STFT), approximate string matching (ASM), window, basic operatio

Multimodal music information processing and retrieval: survey and future challenges

Towards improving the performance in various music information processing tasks, recent studies exploit different modalities able to capture diverse aspects of music. Such modalities include audio recordings, symbolic music scores, mid-level representations, motion, and gestural data, video recordings, editorial or cultural tags, lyrics and album cover arts. This paper critically reviews the various approaches adopted in Music Information Processing and Retrieval and highlights how multimodal algorithms can help Music Computing applications. First, we categorize the related literature based on the application they address. Subsequently, we analyze existing information fusion approaches, and we conclude with the set of challenges that Music Information Retrieval and Sound and Music Computing research communities should focus in the next years

Towards an All-Purpose Content-Based Multimedia Information Retrieval System

The growth of multimedia collections - in terms of size, heterogeneity, and variety of media types - necessitates systems that are able to conjointly deal with several forms of media, especially when it comes to searching for particular objects. However, existing retrieval systems are organized in silos and treat different media types separately. As a consequence, retrieval across media types is either not supported at all or subject to major limitations. In this paper, we present vitrivr, a content-based multimedia information retrieval stack. As opposed to the keyword search approach implemented by most media management systems, vitrivr makes direct use of the object's content to facilitate different types of similarity search, such as Query-by-Example or Query-by-Sketch, for and, most importantly, across different media types - namely, images, audio, videos, and 3D models. Furthermore, we introduce a new web-based user interface that enables easy-to-use, multimodal retrieval from and browsing in mixed media collections. The effectiveness of vitrivr is shown on the basis of a user study that involves different query and media types. To the best of our knowledge, the full vitrivr stack is unique in that it is the first multimedia retrieval system that seamlessly integrates support for four different types of media. As such, it paves the way towards an all-purpose, content-based multimedia information retrieval system

Progressive Filtering Using Multiresolution Histograms for Query by Humming System

The rising availability of digital music stipulates effective categorization and retrieval methods. Real world scenarios are characterized by mammoth music collections through pertinent and non-pertinent songs with reference to the user input. The primary goal of the research work is to counter balance the perilous impact of non-relevant songs through Progressive Filtering (PF) for Query by Humming (QBH) system. PF is a technique of problem solving through reduced space. This paper presents the concept of PF and its efficient design based on Multi-Resolution Histograms (MRH) to accomplish searching in manifolds. Initially the entire music database is searched to obtain high recall rate and narrowed search space. Later steps accomplish slow search in the reduced periphery and achieve additional accuracy. Experimentation on large music database using recursive programming substantiates the potential of the method. The outcome of proposed strategy glimpses that MRH effectively locate the patterns. Distances of MRH at lower level are the lower bounds of the distances at higher level, which guarantees evasion of false dismissals during PF. In due course, proposed method helps to strike a balance between efficiency and effectiveness. The system is scalable for large music retrieval systems and also data driven for performance optimization as an added advantage.Comment: 12 Pages, 6 Figures, Full version of the paper published at ICMCCA-2012 with the same title, Link:http://link.springer.com/chapter/10.1007/978-81-322-1143-3_2