Music is built from sound, ultimately resulting from an elaborate interaction between the sound-generating properties of physical objects (i.e. music instruments) and the sound perception abilities of the human auditory system. Humans, even without any kind of formal music training, are typically able to ex- tract, almost unconsciously, a great amount of relevant information from a musical signal. Features such as the beat of a musical piece, the main melody of a complex musical ar- rangement, the sound sources and events occurring in a complex musical mixture, the song structure (e.g. verse, chorus, bridge) and the musical genre of a piece, are just some examples of the level of knowledge that a naive listener is commonly able to extract just from listening to a musical piece. In order to do so, the human auditory system uses a variety of cues ...

In the world of Information and communications technology (ICT), strategies for innovation and development are increasingly focusing on applications that require spatial representation and real-time interaction with and within 3D-media environments. One of the major challenges that such applications have to address is user-centricity, reflecting e.g. on developing complexity-hiding services so that people can personalize their own delivery of services. In these terms, multimodal interfaces represent a key factor for enabling an inclusive use of new technologies by everyone. In order to achieve this, multimodal realistic models that describe our environment are needed, and in particular models that accurately describe the acoustics of the environment and communication through the auditory modality are required. Examples of currently active research directions and application areas include 3DTV and future internet, 3D visual-sound scene coding, transmission and reconstruction and teleconferencing systems, to name but ...

The problem of segregating a sound source of interest from an acoustic background has been extensively studied due to applications in hearing prostheses, robust speech/speaker recognition and audio information retrieval. Computational auditory scene analysis (CASA) approaches the segregation problem by utilizing grouping cues involved in the perceptual organization of sound by human listeners. Binaural processing, where input signals resemble those that enter the two ears, is of particular interest in the CASA field. The dominant approach to binaural segregation has been to derive spatially selective filters in order to enhance the signal in a direction of interest. As such, the problems of sound localization and sound segregation are closely tied. While spatial filtering has been widely utilized, substantial performance degradation is incurred in reverberant environments and more fundamentally, segregation cannot be performed without sufficient spatial separation between sources. This dissertation ...

During the last decades, information is being gathered and processed at an explosive rate. This fact gives rise to a very important issue, that is, how to effectively and precisely describe the information content of a given source signal or an ensemble of source signals, such that it can be stored, processed or transmitted by taking into consideration the limitations and capabilities of the several digital devices. One of the fundamental principles of signal processing for decades is the Nyquist-Shannon sampling theorem, which states that the minimum number of samples needed to reconstruct a signal without error is dictated by its bandwidth. However, there are many cases in our everyday life in which sampling at the Nyquist rate results in too many data and thus, demanding an increased processing power, as well as storage requirements. A mathematical theory that emerged ...

Everyday sounds in real-world environments are a powerful source of information by which humans can interact with their environments. Humans can infer what is happening around them by listening to everyday sounds. At the same time, it is a challenging task for a computer algorithm in a smart device to automatically recognise, understand, and interpret everyday sounds. Sound event detection (SED) is the process of transcribing an audio recording into sound event tags with onset and offset time values. This involves classification and segmentation of sound events in the given audio recording. SED has numerous applications in everyday life which include security and surveillance, automation, healthcare monitoring, multimedia information retrieval, and assisted living technologies. SED is to everyday sounds what automatic speech recognition (ASR) is to speech and automatic music transcription (AMT) is to music. The fundamental questions in designing ...

Reverberation is the sum of reflected sound waves and is present in any conventional room. Speech communication devices such as mobile phones in hands-free mode, tablets, smart TVs, teleconferencing systems, hearing aids, voice-controlled systems, etc. use one or more microphones to pick up the desired speech signals. When the microphones are not in the proximity of the desired source, strong reverberation and noise can degrade the signal quality at the microphones and can impair the intelligibility and the performance of automatic speech recognizers. Therefore, it is a highly demanded task to process the microphone signals such that reverberation and noise are reduced. The process of reducing or removing reverberation from recorded signals is called dereverberation. As dereverberation is usually a completely blind problem, where the only available information are the microphone signals, and as the acoustic scenario can be non-stationary, ...

The overriding aim of this thesis is to investigate the benefits of focusing time-frequency analysis on particular regions of the time-frequency plane. The thesis examines aspects of such a regionalisation in the analysis of both deterministic signals and stochastic processes. The majority of deterministic energetic time-frequency representations are non-parametric indicating the distribution of the energy of a signal in the time-frequency plane but providing no further information about the time-frequency structure. This thesis develops a semi-parametric time-frequency model to simultaneously describe the time-frequency energetic structure of a signal and provide an indication of its time-frequency complexity. The model aims to identify ‘timefrequency components’ within the signal to indicate how their energy is distributed in the time-frequency plane and thereby to probabilistically associate every location in the plane with each identified component. The thesis investigates a number of applications of the ...

In recent years, inertial sensors have undergone major developments. The quality of their measurements has improved while their cost has decreased, leading to an increase in availability. They can be found in stand-alone sensor units, so-called inertial measurement units, but are nowadays also present in for instance any modern smartphone, in Wii controllers and in virtual reality headsets. The term inertial sensor refers to the combination of accelerometers and gyroscopes. These measure the external specific force and the angular velocity, respectively. Integration of their measurements provides information about the sensor’s position and orientation. However, the position and orientation estimates obtained by simple integration suffer from drift and are therefore only accurate on a short time scale. In order to improve these estimates, we combine the inertial sensors with additional sensors and models. To combine these different sources of information, also ...

The subject of this PhD thesis is the efficient and robust processing and analysis of the audio recordings that are derived from a call center. The thesis is comprised of two parts. The first part is dedicated to dialogue/non-dialogue detection and to speaker segmentation. The systems that are developed are prerequisite for detecting (i) the audio segments that actually contain a dialogue between the system and the call center customer and (ii) the change points between the system and the customer. This way the volume of the audio recordings that need to be processed is significantly reduced, while the system is automated. To detect the presence of a dialogue several systems are developed. This is the first effort found in the international literature that the audio channel is exclusively exploited. Also, it is the first time that the speaker utterance ...

This thesis investigates the design and evaluation of an embedded optimization framework for the perceptual enhancement of audio signals which are degraded by linear and/or nonlinear distortion. In general, audio signal enhancement has the goal to improve the perceived audio quality, speech intelligibility, or another desired perceptual attribute of the distorted audio signal by applying a real-time digital signal processing algorithm. In the designed embedded optimization framework, the audio signal enhancement problem under consideration is formulated and solved as a per-frame numerical optimization problem, allowing to compute the enhanced audio signal frame that is optimal according to a desired perceptual attribute. The first stage of the embedded optimization framework consists in the formulation of the per-frame optimization problem aimed at maximally enhancing the desired perceptual attribute, by explicitly incorporating a suitable model of human sound perception. The second stage of ...

Single channel source separation is a quite recent problem of constantly growing interest in the scientific world. However, this problem is still very far to be solved, and even more, it cannot be solved in all its generality. Indeed, since this problem is highly underdetermined, the main difficulty is that a very strong knowledge about the sources is required to be able to separate them. For a grand class of existing separation methods, this knowledge is expressed by statistical source models, notably Gaussian Mixture Models (GMM), which are learned from some training examples. The subject of this work is to study the separation methods based on statistical models in general, and then to apply them to the particular problem of separating singing voice from background music in mono recordings of songs. It can be very useful to propose some satisfactory ...

Audio effects modeling is the process of emulating an audio effect unit and seeks to recreate the sound, behaviour and main perceptual features of an analog reference device. Audio effect units are analog or digital signal processing systems that transform certain characteristics of the sound source. These transformations can be linear or nonlinear, time-invariant or time-varying and with short-term and long-term memory. Most typical audio effect transformations are based on dynamics, such as compression; tone such as distortion; frequency such as equalization; and time such as artificial reverberation or modulation based audio effects. The digital simulation of these audio processors is normally done by designing mathematical models of these systems. This is often difficult because it seeks to accurately model all components within the effect unit, which usually contains mechanical elements together with nonlinear and time-varying analog electronics. Most existing ...

This thesis faces the problem of automatically classifying environmental sounds, i.e., any non-speech or non-music sounds that can be found in the environment. Broadly speaking, two main processes are needed to perform such classification: the signal feature extraction so as to compose representative sound patterns and the machine learning technique that performs the classification of such patterns. The main focus of this research is put on the former, studying relevant signal features that optimally represent the sound characteristics since, according to several references, it is a key issue to attain a robust recognition. This type of audio signals holds many differences with speech or music signals, thus specific features should be determined and adapted to their own characteristics. In this sense, new signal features, inspired by the human auditory system and the human perception of sound, are proposed to improve ...

This thesis deals with the efficient and flexible acquisition and processing of spatial sound using multiple microphones. In spatial sound acquisition and processing, we use multiple microphones to capture the sound of multiple sources being simultaneously active at a rever- berant recording side and process the sound depending on the application at the application side. Typical applications include source extraction, immersive spatial sound reproduction, or speech enhancement. A flexible sound acquisition and processing means that we can capture the sound with almost arbitrary microphone configurations without constraining the application at the ap- plication side. This means that we can realize and adjust the different applications indepen- dently of the microphone configuration used at the recording side. For example in spatial sound reproduction, where we aim at reproducing the sound such that the listener perceives the same impression as if he ...

In the modern world, we are increasingly surrounded by computation devices with communication links and one or more microphones. Such devices are, for example, smartphones, tablets, laptops or hearing aids. These devices can work together as nodes in an acoustic sensor network (ASN). Such networks are a growing platform that opens the possibility for many practical applications. ASN based speech enhancement, source localization, and event detection can be applied for teleconferencing, camera control, automation, or assisted living. For this kind of applications, the awareness of auditory objects and their spatial positioning are key properties. In order to provide these two kinds of information, novel methods have been developed in this thesis. Information on the type of auditory objects is provided by a novel real-time sound classification method. Information on the position of human speakers is provided by a novel localization ...