Exploring microtonal matching

Most research intomusic information retrieval thus far has only examined music from the western tradition. However, music of other origins often conforms to different tuning systems. Therefore there are problems both in representing this music as well as finding matches to queries from these diverse tuning systems. We discuss the issues associated with microtonal music retrieval and present some preliminary results from an experiment in applying scoring matrices to microtonal matching

MIRMaid: An interface for a content based Music Information Retrieval test-bed

Music Information Retrieval (MlR) is the interdisciplinary science of retrieving information from music and includes influences from different areas, like music perception and cognition, music analysis, signal processing, music indexing and information retrieval [Futrelle & Downie, 2003]. To produce the most efficient MlR systems, test-beds are commonly used to test different combinations of parameters against each other. The purpose of this dissertation was to investigate the composition of algorithms for MlR systems by constructing an interface that could form part of a test-bed. It differs from other interfaces and frameworks that are used in MlR test-beds because it is focused on small scale test-beds. MIRMaid is an acronym for Music Information Retrieval Modular aid and is an interface that allows different content based retrieval tasks to be compared against each other to find optimal combinations of retrieval parameters for specialised problem domains. The dissertation describes the process of how the MIRMaid interface was developed, modified and refined. A big challenge was to design the user experiments in a way that considered potential users of the interface while using the test subjects I had at my disposal. I decided to use the simplest queries to highlight basic similarities between novice and potential expert users. The performance of the interface was judged by user ratings on a questionnaire. The interface performed reasonably well with expert users and novice users. Despite these results there were a few interesting observations that were returned from the user experiments related to the experiment design and the task explanations. Some suggestions are also provided for extending the interface to allow it to be used with other types of data. The possibility is also investigated for using the interface as a tool for simplifying the process of integrating modules from different sources

Optimizing Melodic Extraction Algorithm for Jazz Guitar Recordings Using Genetic Algorithms

(Abstract to follow

Toward the Scientific Evaluation of Music Information Retrieval Systems

This paper outlines the findings-to-date of a project to assist in the efforts being made to establish a TREC-like evaluation paradigm within the Music Information Retrieval (MIR) research community. The findings and recommendations are based upon expert opinion garnered from members of the Information Retrieval (IR), Music Digital Library (MDL) and MIR communities with regard to the construction and implementation of scientifically valid evaluation frameworks. Proposed recommendations include the creation of data-rich query records that are both grounded in real-world requirements and neutral with respect to retrieval technique(s) being examined; adoption, and subsequent validation, of a “reasonable person” approach to “relevance” assessment; and, the development of a secure, yet accessible, research environment that allows researchers to remotely access the large-scale testbed collection

What does the Mongeau-Sankoff algorithm compute?

How similar are two melodies? Proposed in 1990, the Mongeau-Sankoff algorithm computes the best alignment between two melodies with insertion, deletion, substitution , fragmentation, and consolidation operations. This popular algorithm is sometimes misunderstood. Indeed, computing the best edit distance, which is the best chain of operations, is a more elaborated problem. Our objective is to clarify the usage of the Mongeau-Sankoff algorithm. In particular, we observe that an alignment is a restricted case of edition. This is especially the case when some edit operations overlap, e.g. when one further changes one or several notes resulting of a fragmentation or a consolidation. We propose recommendations for people wanting to use or extend this algorithm, and discuss the design of combined or extended operations, with specific costs

Algoritmeja melodian etsimiseen ja nuotinnukseen

This thesis studies two problems in music information retrieval: search for a given melody in an audio database, and automatic melody transcription. In both of the problems, the representation of the melody is symbolic, i.e., the melody consists of onset times and pitches of musical notes. In the first part of the thesis we present new algorithms for symbolic melody search. First, we present algorithms that work with a matrix representation of the audio data, that corresponds to the discrete Fourier transform. We formulate the melody search problem as a generalization of the classical maximum subarray problem. After this, we discuss algorithms that operate on a geometric representation of the audio data. In this case, the Fourier transform is converted into a set of points in the two-dimensional plane. The main contributions of the first part of the thesis lie in algorithm design. We present new efficient algorithms, most of which are based on dynamic programming optimization, i.e., calculating dynamic programming values more efficiently using appropriate data structures and algorithm design techniques. Finally, we experiment with the algorithms using real-world audio databases and melody queries, which shows that the algorithms can be successfully used in practice. Compared to previous melody search systems, the novelty in our approach is that the search can be performed directly in the Fourier transform of the audio data. The second part of the thesis focuses on automatic melody transcription. As this problem is very difficult in its pure form, we ask whether using certain additional information would facilitate the transcription. We present two melody transcription systems that extract the main melodic line from an audio signal using additional information. The first transcription system utilizes as additional information an initial transcription created by the human user of the system. It turns out that users without a musical background are able to provide the system with useful information about the melody, so that the transcription quality increases considerably. The second system takes a chord transcription as additional information, and produces a melody transcription that matches both the audio signal and the harmony given in the chord transcription. Our system is a proof of concept that the connection between melody and harmony can be used in automatic melody transcription.Väitöskirjan aiheena on kaksi musiikkitiedonhaun ongelmaa: melodian etsiminen audiotietokannasta sekä automaattinen melodian nuotinnus. Molemmissa ongelmissa melodia on esitetty symbolisesti eli melodia muodostuu nuottien alkukohdista ja korkeuksista. Väitöskirjan alkuosa esittelee uusia algoritmeja symbolisen melodian etsimiseen. Ensin tarkastelussa on tilanne, jossa audiodata on diskreettiä Fourier-muunnosta vastaavassa matriisimuodossa. Tällöin melodian etsiminen voidaan nähdä yleistyksenä klassisesta taulukon suurimman summan tuottavan välin etsimisestä. Tämän jälkeen käsittely siirtyy algoritmeihin, joissa audiodata on esitetty geometrisesti kaksiulotteisen tason pistejoukkona. Tärkeimmät kontribuutiot väitöskirjan alkuosassa liittyvät algoritmien suunnitteluun. Väitöskirja esittelee uusia tehokkaita algoritmeja, joista useimmat perustuvat dynaamisen ohjelmoinnin optimointiin. Tämä tarkoittaa, että dynaamisen ohjelmoinnin arvoja lasketaan tavallista tehokkaammin käyttämällä sopivia tietorakenteita ja algoritmien suunnittelun tekniikoita. Algoritmeja myös testataan todellisilla audiotietokannoilla ja melodiahauilla, mikä osoittaa niiden toimivuuden käytännössä. Verrattuna aiempiin tutkimuksiin väitöskirjan lähestymistavan etuna on, että melodian haku voidaan kohdistaa suoraan audiodatan Fourier-muunnokseen. Väitöskirjan jälkiosa keskittyy automaattiseen melodian nuotinnukseen. Koska ongelma on hyvin vaikea sellaisenaan, tutkimuskysymyksenä on, miten nuotinnusta voi helpottaa käyttämällä musiikillista lisätietoa. Väitöskirja esittelee kaksi melodian nuotinnukseen tarkoitettua järjestelmää, jotka pyrkivät erottamaan tärkeimmän melodialinjan audiosignaalista musiikillisen lisätiedon avulla. Ensimmäinen järjestelmä käyttää lisätietona ihmiskäyttäjän arvioita nuottien alkukohdista ja korkeuksista. Osoittautuu, että käyttäjät, joilla ei ole musiikkitaustaa, pystyvät tarjoamaan järjestelmälle hyödyllistä lisätietoa, jonka avulla nuotinnuksen laatu parantuu merkittävästi. Toisen järjestelmän lisätietona on sointukulku, joka kuvaa musiikin harmoniaa. Järjestelmä tuottaa nuotinnuksen, joka perustuu sekä audiosignaaliin että sointukulkuun. Järjestelmä on osoitus siitä, että melodian ja harmonian yhteyttä voidaan hyödyntää automaattisessa melodian nuotinnuksessa