Music fingerprinting based on bhattacharya distance for song and cover song recognition

People often have trouble recognizing a song especially, if the song is sung by a not original artist which is called cover song. Hence, an identification system might be used to help recognize a song or to detect copyright violation. In this study, we try to recognize a song and a cover song by using the fingerprint of the song represented by features extracted from MPEG-7. The fingerprint of the song is represented by Audio Signature Type. Moreover, the fingerprint of the cover song is represented by Audio Spectrum Flatness and Audio Spectrum Projection. Furthermore, we propose a sliding algorithm and k-Nearest Neighbor (k-NN) with Bhattacharyya distance for song recognition and cover song recognition. The results of this experiment show that the proposed fingerprint technique has an accuracy of 100% for song recognition and an accuracy of 85.3% for cover song recognition

LARGE-SCALE COVER SONG RECOGNITION USING HASHED CHROMA LANDMARKS

Cover song recognition, also known as version identification, can only be solved by exposing the underlying tonal content of music. Apart from obvious applications in copyright enforcement, techniques for cover identification can also be used to find patterns and structure in music datasets too large for any musicologist to listen to even once. Much progress has been made on cover song recognition, but work to date has been reported on datasets of at most a few thousand songs, using algorithms that simply do not scale beyond the capacity of a small portable music player. In this paper, we consider the problem of finding covers in a database of a million songs, considering only algorithms that can deal with such data. Using a fingerprinting-inspired model, we present the first results of cover song recognition on the Million Song Dataset. The availability of industrial-scale datasets to the research community presents a new frontier for version identification, and this work is intended to be the first step toward a practical solution. Index Terms — Cover song, fingerprinting, music identification

Literary review of content-based music recognition paradigms

During the last few decades, a need for novel retrieval strategies for large audio databases emerged as millions of digital audio documents became accessible for everyone through the Internet. It became essential that the users could search for songs that they had no prior information about using only the content of the audio as a query. In practice this means that when a user hears an unknown song coming out of the radio and wants to get more information about it, he or she can simply record a sample of the song with a mobile device and send it to a music recognition application as a query. Query results would then be presented on the screen with all the necessary meta data, such as the song name and artist. The retrieval systems are expected to perform quickly and accurately against large databases that may contain millions of songs, which poses lots of challenges for the researchers. This thesis is a literature review which will go through some audio retrieval paradigms that allow querying for songs using only their audio content, such as audio fingerprinting. It will also address the typical problems and challenges of audio retrieval and compare how each of these proposed paradigms performs in these challenging scenarios

Rancang Bangun Aplikasi MusicMoo dengan Metode MIR (Music Information Retrieval) pada Modul Fingerprint, Cover Song Recognition, dan Song Recommendation

Industri musik sudah mulai merambah ke bidang komputer. Salah satunya adalah aplikasi SoundHound, Shazam , dan masih banyak lagi. Namun semua aplikasi tersebut hanya melakukan deteksi dari potongan suara yang direkam. Semua aplikasi tersebut bekerja dengan cara melakukan ekstrak fingerprint hanya dari beberapa segmen sinyal audio yang direkam. Pertama, penulis membangun database yang berisi fitur lagu. Di dalam fitur tersebut terdapat kumpulan nilai yang mengidentifikasikan suatu lagu. Dari deskripsi ini digunakan untuk melakukan pencarian pada suatu lagu. Kedua, melakukan proses pada fitur audio yang terkait. Ketiga, melakukan klasifikasi dan hasilnya adalah detail informasi fingerprint, cover song, dan rekomendasi pada suatu lagu. ================================ Music industry has started using computer engineering factor to produce the best quality of an audio . The example of application using musi c as their main theme was SoundH ound, Shazam, and etc . From application example above, they just try to identify the original song from its pieces of recorded song . All applicati on example above works by extracting fingerprint on a few segments of a recorded audio signal . First, writes construct a database contain of some feature of an audio . An audio feature contain of set value that used to describe an audio . This s et of value was used to search a song . Second, to process related audio feature . Third, to classify and the result was information of Fingerprint, Cover Song, and Recommendation of an audio

Identificación de versiones musicales (covers) utilizando aprendizaje maquinal

The task of recognizing when a song is a version or cover of another is a relatively easy task to do for humans when the song is known. However, to cause that a machine perform this work is complex due to the number of variables involved in preparing the cover, including variations in rhythm, tempo, instrumentation, genre and duration compared to the original version. In this project a methodology to identify covers from the application and analysis of machine-learning techniques, statistical signal processing and second order statistics was developed, in order to get that configuration to give the best results. For this we worked with the database Dataset Million Songs that gave us the metadata of the songs, from which data belonging to the acoustic characteristics of the song, such as pitches and timbres were used. Throughout the project we experimented with different data treatment techniques applied to the metadata provided by the database and we could see its usefulness to the task at hand. According to the results, a system that integrates processing frequency on pitches aligned with the beat, the implementation of a sparse coding and a data clustering system that showed a 63% correct identification of covers was obtained. Information on the possible use of supervised learning techniques combined with different types of metrics giving rise to future experiments to improve the results was also obtained.La tarea de reconocer cuándo una canción es una versión o cover de otra es una tarea relativamente fácil para el ser humano cuando se conoce la canción. Sin embargo, hacer que una máquina realice este trabajo resulta complejo debido al número de variables que se involucran en la elaboración del cover, mismas que incluyen variaciones en el ritmo, tempo, instrumentación, género y duración con respecto a la versión original. En este proyecto se desarrolló una metodología para identificar covers a partir de la aplicación y análisis de técnicas de aprendizaje maquinal, procesamiento de señales y estadística de segundo orden con la finalidad de obtener aquella configuración que diera los mejores resultados. Para esto se trabajó con la base de datos Million Songs Dataset que nos otorgó los metadatos de las canciones, a partir de los cuales se utilizaron los datos pertenecientes a las características acústicas de la canción, tales como, pitches y timbres. A lo largo del proyecto se experimentó con diferentes técnicas de tratamiento de los metadatos que proporcionó la base de datos y se pudo apreciar su utilidad para la tarea a desarrollar. De acuerdo a los resultados obtenidos, se obtuvo un sistema que integra un procesamiento en frecuencia sobre los pitches alineados con el beat, la aplicación de una codificación rala y un sistema de agrupamiento de datos que arrojó un 63% de identificación correcta de covers. También se obtuvo información sobre el posible uso de técnicas combinadas de aprendizaje supervisado con diferentes tipos de métricas dando pie a futuras experimentaciones para mejorar los resultados