108 research outputs found
Acoustic event detection for multiple overlapping similar sources
Many current paradigms for acoustic event detection (AED) are not adapted to
the organic variability of natural sounds, and/or they assume a limit on the
number of simultaneous sources: often only one source, or one source of each
type, may be active. These aspects are highly undesirable for applications such
as bird population monitoring. We introduce a simple method modelling the
onsets, durations and offsets of acoustic events to avoid intrinsic limits on
polyphony or on inter-event temporal patterns. We evaluate the method in a case
study with over 3000 zebra finch calls. In comparison against a HMM-based
method we find it more accurate at recovering acoustic events, and more robust
for estimating calling rates.Comment: Accepted for WASPAA 201
An open dataset for research on audio field recording archives: freefield1010
We introduce a free and open dataset of 7690 audio clips sampled from the
field-recording tag in the Freesound audio archive. The dataset is designed for
use in research related to data mining in audio archives of field recordings /
soundscapes. Audio is standardised, and audio and metadata are Creative Commons
licensed. We describe the data preparation process, characterise the dataset
descriptively, and illustrate its use through an auto-tagging experiment
Making music through real-time voice timbre analysis: machine learning and timbral control
PhDPeople can achieve rich musical expression through vocal sound { see for example
human beatboxing, which achieves a wide timbral variety through a range of
extended techniques. Yet the vocal modality is under-exploited as a controller
for music systems. If we can analyse a vocal performance suitably in real time,
then this information could be used to create voice-based interfaces with the
potential for intuitive and ful lling levels of expressive control.
Conversely, many modern techniques for music synthesis do not imply any
particular interface. Should a given parameter be controlled via a MIDI keyboard,
or a slider/fader, or a rotary dial? Automatic vocal analysis could provide
a fruitful basis for expressive interfaces to such electronic musical instruments.
The principal questions in applying vocal-based control are how to extract
musically meaningful information from the voice signal in real time, and how
to convert that information suitably into control data. In this thesis we address
these questions, with a focus on timbral control, and in particular we
develop approaches that can be used with a wide variety of musical instruments
by applying machine learning techniques to automatically derive the mappings
between expressive audio input and control output. The vocal audio signal is
construed to include a broad range of expression, in particular encompassing
the extended techniques used in human beatboxing.
The central contribution of this work is the application of supervised and
unsupervised machine learning techniques to automatically map vocal timbre
to synthesiser timbre and controls. Component contributions include a delayed
decision-making strategy for low-latency sound classi cation, a regression-tree
method to learn associations between regions of two unlabelled datasets, a fast
estimator of multidimensional di erential entropy and a qualitative method for
evaluating musical interfaces based on discourse analysis
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