Stefan Kersten

Expressive concatenative synthesis by reusing samples from real performance recordings

Here we describe an approach to the expressive synthesis of jazz saxophone melodies that reuses audio recordings and carefully concatenates note samples. The aim is to generate an expressive audio sequence from the analysis of an arbitrary input score using a previously induced performance model and an annotated saxophone note database extracted from real performances. We push the idea of using the same corpus for both inducing an expressive performance model and synthesizing sound by concatenating samples in the corpus. Therefore, a connection between the performers’ instrument sound and performance characteristics is kept during the synthesis process.

Ecological acoustics perspective for content-based retrieval of environmental sounds

In this paper we present a method to search for environmental sounds in large unstructured databases of user-submitted audio, using a general sound events taxonomy from ecological acoustics. We discuss the use of Support Vector Machines to classify sound recordings according to the taxonomy and describe two use cases for the obtained classification models: a content-based web search interface for a large audio database and a method for segmenting field recordings to assist sound design.

Genre classification of music by tonal harmony

In this paper we present a genre classification framework for audio music based on a symbolic classification system. Audio signals are transformed into a symbolic representation of harmony using a chord transcription algorithm, based on the computation of harmonic pitch class profiles. Then, language models built from a ground truth of chord progressions for each genre are used to perform classification. We show that chord progressions are a suitable feature to represent musical genre, as they capture the harmonic rules relevant in each musical period or style. Finally, results using both pure symbolic information and chords transcribed from audio-from-MIDI are compared, in order to evaluate the effects of the transcription process in this task.

Modeling violin performances using inductive logic programming

Professional musicians intuitively manipulate sound properties such as pitch, timing, amplitude and timbre in order to produce expressive performances of particular pieces. However, there is little explicit information about how and in which musical contexts this manipulation occurs. In this paper we describe a machine learning approach to modeling the knowledge applied by a musician when performing a score in order to produce an expressive performance. In particular, we apply inductive logic programming techniques in order to automatically learn models for both understanding and generating expressive violin performances.

Towards equalization of environmental sounds using auditory-based features

In this paper we describe methods to assist soundscape design, sound production and processing for interactive environments, like games and simulations. Using auditory filter banks and sound texture synthesis, we develop algorithms that can be integrated with existing audio engines and can additionally support the development of dedicated high-level audio tools aimed at content authoring or transformations based on samples. The relationship between the auditory excitation patterns and the computation algorithm is explained within the context of footstep sounds. Moreover, methods for sound texture synthesis of water streams with artificial expansion of timbre space using auditory filtering techniques are presented.