A Feature Learning Siamese Model for Intelligent Control of the Dynamic Range Compressor

In this paper, a siamese DNN model is proposed to learn the characteristics of the audio dynamic range compressor (DRC). This facilitates an intelligent control system that uses audio examples to configure the DRC, a widely used non-linear audio signal conditioning technique in the areas of music production, speech communication and broadcasting. Several alternative siamese DNN architectures are proposed to learn feature embeddings that can characterise subtle effects due to dynamic range compression. These models are compared with each other as well as handcrafted features proposed in previous work. The evaluation of the relations between the hyperparameters of DNN and DRC parameters are also provided. The best model is able to produce a universal feature embedding that is capable of predicting multiple DRC parameters simultaneously, which is a significant improvement from our previous research. The feature embedding shows better performance than handcrafted audio features when predicting DRC parameters for both mono-instrument audio loops and polyphonic music pieces.Comment: 8 pages, accepted in IJCNN 201

Haptics for the development of fundamental rhythm skills, including multi-limb coordination

This chapter considers the use of haptics for learning fundamental rhythm skills, including skills that depend on multi-limb coordination. Different sensory modalities have different strengths and weaknesses for the development of skills related to rhythm. For example, vision has low temporal resolution and performs poorly for tracking rhythms in real-time, whereas hearing is highly accurate. However, in the case of multi-limbed rhythms, neither hearing nor sight are particularly well suited to communicating exactly which limb does what and when, or how the limbs coordinate. By contrast, haptics can work especially well in this area, by applying haptic signals independently to each limb. We review relevant theories, including embodied interaction and biological entrainment. We present a range of applications of the Haptic Bracelets, which are computer-controlled wireless vibrotactile devices, one attached to each wrist and ankle. Haptic pulses are used to guide users in playing rhythmic patterns that require multi-limb coordination. One immediate aim of the system is to support the development of practical rhythm skills and multi-limb coordination. A longer-term goal is to aid the development of a wider range of fundamental rhythm skills including recognising, identifying, memorising, retaining, analysing, reproducing, coordinating, modifying and creating rhythms – particularly multi-stream (i.e. polyphonic) rhythmic sequences. Empirical results are presented. We reflect on related work, and discuss design issues for using haptics to support rhythm skills. Skills of this kind are essential not just to drummers and percussionists but also to keyboards players, and more generally to all musicians who need a firm grasp of rhythm

The development of corpus-based computer assisted composition program and its application for instrumental music composition

In the last 20 years, we have seen the nourishing environment for the development of music software using a corpus of audio data expanding significantly, namely that synthesis techniques producing electronic sounds, and supportive tools for creative activities are the driving forces to the growth. Some software produces a sequence of sounds by means of synthesizing a chunk of source audio data retrieved from an audio database according to a rule. Since the matching of sources is processed according to their descriptive features extracted by FFT analysis, the quality of the result is significantly influenced by the outcomes of the Audio Analysis, Segmentation, and Decomposition. Also, the synthesis process often requires a considerable amount of sample data and this can become an obstacle to establish easy, inexpensive, and user-friendly applications on various kinds of devices. Therefore, it is crucial to consider how to treat the data and construct an efficient database for the synthesis. We aim to apply corpusbased synthesis techniques to develop a Computer Assisted Composition program, and to investigate the actual application of the program on ensemble pieces. The goal of this research is to apply the program to the instrumental music composition, refine its function, and search new avenues for innovative compositional method

Automatic Drum Transcription and Source Separation

While research has been carried out on automated polyphonic music transcription, to-date the problem of automated polyphonic percussion transcription has not received the same degree of attention. A related problem is that of sound source separation, which attempts to separate a mixture signal into its constituent sources. This thesis focuses on the task of polyphonic percussion transcription and sound source separation of a limited set of drum instruments, namely the drums found in the standard rock/pop drum kit. As there was little previous research on polyphonic percussion transcription a broad review of music information retrieval methods, including previous polyphonic percussion systems, was also carried out to determine if there were any methods which were of potential use in the area of polyphonic drum transcription. Following on from this a review was conducted of general source separation and redundancy reduction techniques, such as Independent Component Analysis and Independent Subspace Analysis, as these techniques have shown potential in separating mixtures of sources. Upon completion of the review it was decided that a combination of the blind separation approach, Independent Subspace Analysis (ISA), with the use of prior knowledge as used in music information retrieval methods, was the best approach to tackling the problem of polyphonic percussion transcription as well as that of sound source separation. A number of new algorithms which combine the use of prior knowledge with the source separation abilities of techniques such as ISA are presented. These include sub-band ISA, Prior Subspace Analysis (PSA), and an automatic modelling and grouping technique which is used in conjunction with PSA to perform polyphonic percussion transcription. These approaches are demonstrated to be effective in the task of polyphonic percussion transcription, and PSA is also demonstrated to be capable of transcribing drums in the presence of pitched instruments

Music content analysis: Key, chord and rhythm tracking in acoustic signals

Master'sMASTER OF SCIENC

Automatic characterization and generation of music loops and instrument samples for electronic music production

Repurposing audio material to create new music - also known as sampling - was a foundation of electronic music and is a fundamental component of this practice. Currently, large-scale databases of audio offer vast collections of audio material for users to work with. The navigation on these databases is heavily focused on hierarchical tree directories. Consequently, sound retrieval is tiresome and often identified as an undesired interruption in the creative process. We address two fundamental methods for navigating sounds: characterization and generation. Characterizing loops and one-shots in terms of instruments or instrumentation allows for organizing unstructured collections and a faster retrieval for music-making. The generation of loops and one-shot sounds enables the creation of new sounds not present in an audio collection through interpolation or modification of the existing material. To achieve this, we employ deep-learning-based data-driven methodologies for classification and generation.Repurposing audio material to create new music - also known as sampling - was a foundation of electronic music and is a fundamental component of this practice. Currently, large-scale databases of audio offer vast collections of audio material for users to work with. The navigation on these databases is heavily focused on hierarchical tree directories. Consequently, sound retrieval is tiresome and often identified as an undesired interruption in the creative process. We address two fundamental methods for navigating sounds: characterization and generation. Characterizing loops and one-shots in terms of instruments or instrumentation allows for organizing unstructured collections and a faster retrieval for music-making. The generation of loops and one-shot sounds enables the creation of new sounds not present in an audio collection through interpolation or modification of the existing material. To achieve this, we employ deep-learning-based data-driven methodologies for classification and generation

Evaluation of Drum Rhythmspace in a Music Production Environment

In modern computer-based music production, vast musical data libraries are essential. However, their presentation via subpar interfaces can hinder creativity, complicating the selection of ideal sequences. While low-dimensional space solutions have been suggested, their evaluations in real-world music production remain limited. In this study, we focus on Rhythmspace, a two-dimensional platform tailored for the exploration and generation of drum patterns in symbolic MIDI format. Our primary objectives encompass two main aspects: first, the evolution of Rhythmspace into a VST tool specifically designed for music production settings, and second, a thorough evaluation of this tool to ascertain its performance and applicability within the music production scenario. The tool’s development necessitated transitioning the existing Rhythmspace, which operates in Puredata and Python, into a VST compatible with Digital Audio Workstations (DAWs) using the JUCE(C++) framework. Our evaluation encompassed a series of experiments, starting with a composition test where participants crafted drum sequences followed by a listening test, wherein participants ranked the sequences from the initial experiment. The results show that Rhythmspace and similar tools are beneficial, facilitating the exploration and creation of drum patterns in a user-friendly and intuitive manner, and enhancing the creative process for music producers. These tools not only streamline the drum sequence generation but also offer a fresh perspective, often serving as a source of inspiration in the dynamic realm of electronic music production

Automatic cymbal classification

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia InformáticaMost of the research on automatic music transcription is focused on the transcription of pitched instruments, like the guitar and the piano. Little attention has been given to unpitched instruments, such as the drum kit, which is a collection of unpitched instruments. Yet, over the last few years this type of instrument started to garner more attention, perhaps due to increasing popularity of the drum kit in the western music. There has been work on automatic music transcription of the drum kit, especially the snare drum, bass drum, and hi-hat. Still, much work has to be done in order to achieve automatic music transcription of all unpitched instruments. An example of a type of unpitched instrument that has very particular acoustic characteristics and that has deserved almost no attention by the research community is the drum kit cymbals. A drum kit contains several cymbals and usually these are treated as a single instrument or are totally disregarded by automatic music classificators of unpitched instruments. We propose to fill this gap and as such, the goal of this dissertation is automatic music classification of drum kit cymbal events, and the identification of which class of cymbals they belong to. As stated, the majority of work developed on this area is mostly done with very different percussive instruments, like the snare drum, bass drum, and hi-hat. On the other hand, cymbals are very similar between them. Their geometry, type of alloys, spectral and sound traits shows us just that. Thus, the great achievement of this work is not only being able to correctly classify the different cymbals, but to be able to identify such similar instruments, which makes this task even harder