Music Neurotechnology

BeliefRecode AI™ introduces an emotion-adaptive artificial intelligence framework designed to detect, analyze, and therapeutically respond to human belief patterns in real time. Rooted in affective computing, cognitive psychology, and narrative therapy, this system enables digital therapeutic interventions for conditions such as paranoid delusion, psychosis, and belief distortion.

Unlike traditional medication or static CBT approaches, BeliefRecode AI™ dynamically “rewrites” maladaptive beliefs by generating adaptive narrative interactions that evolve according to the patient’s emotional state. This approach defines a new discipline called Belief Medicine™ — where AI becomes a compassionate therapeutic companion that heals cognition through emotional understanding.

The study outlines the conceptual foundation, system architecture, clinical use cases, and ethical framework for emotion-adaptive cognitive rewriting. The invention forms the basis for a filed patent under the Indian Patent Office (Application No. 202541097631, 09 October 2025).

Musicians and composers have been using brainwaves as generative sources in music for at least 40 years and the possibility of a brain-computer interface for direct communication and control was first seriously investigated in the early 1970s. Work has been done by many artists and technologists in the intervening years to attempt to control music systems with brainwaves and - indeed - many other biological signals. Despite the richness of EEG, fMRI and other data which can be read from the human brain, there has up to now been only limited success in translating the complex encephalographic data into satisfactory musical results. We are currently pursuing research which we believe will lead to the possibility of direct brain-computer interfaces for rich and expressive musical control. This report will outline the directions of our current research and results.

This paper reports on the development of a proof-of-concept brain-computer music interfacing system (BCMI), which we built to be tested with a patient with Locked-in Syndrome at the Royal Hospital for Neuro-disability, in London. The system uses the Steady State Visual Evoked Potential (SSVEP) method, whereby targets are presented to a user on a computer monitor representing actions available to perform with the system. Each target is encoded by a flashing visual pattern reversing at a unique frequency. In order to make a selection, the user must direct her gaze at the target corresponding to the action she would like to perform. The patient grasped the concept quickly and rapidly demonstrated her skill at controlling the system with minimal practice. She was able to vary the intensity of her gaze, thus changing the amplitude of her EEG and vary the consequent musical parameters. We have proved the concept that such BCMI system is cost-effective to build, viable, and useful. However, ergonomic and design aspects of the system require further refinement in order to make it more practical for clinical usage. For instance, the system at present requires a therapist to place individual electrodes and calibrate a user's response to each stimulus, which can be time consuming. A new version of the system will require just positioning of a headset and, due to advanced algorithms, will require no calibration.

This paper reports the current level of development achieved by our research into brain-computer music interfacing (BCMI), which is aimed at special needs and Music Therapy, in addition to the entertainment industry. It surveys the technology developed to date at ICCMR and glances at work-inprogress designs informed by cognitive experiments. Research into BCMI involves three major challenging problems: (1) the extraction of meaningful control information from signals emanating from the brain, (2) the design of generative music techniques that respond to such information and (3) the definition of ways in which such technology can effectively improve the lives of people with special needs and address therapeutic needs. This paper focuses on the first two challenges, particularly the music technology side of BCMI research. In overall, this paper focuses on the creative computer music component of our research rather than on the minutiae of its underlying scientific protocols and experimental methods.

We are interested in exploring ways in which unconventional modes of computation may provide new directions for future developments in Computer Music. Research into Unconventional Computing (Calude et al. 1998) is aimed at computational paradigms other than the standard von Neumann architecture, which have prevailed in computing since the 1940s. This paper presents an investigation into the feasibility of synthesizing sounds with hybrid wetware-silicon devices using in vitro neuronal networks. The field of Computer Music has evolved in tandem with the field of Computer Science.

The study of neural responses to musical stimuli offers valuable insights into how the brain encodes complex auditory information. This paper compares spectral amplitude modulations from various EEG electrodes, time-locked to attended music, to explore potential correlations. Instead of using traditional 2D TF plots, we utilized a 3D time-frequency-amplitude (TFA) fabric over a 500 ms window, synchronized with the audio of the song. This approach was applied to five songs, examining the TFA fabric across different electrodes. The TFA fabric refers to the continuous manifold created by the frequency-amplitude (FA) curve over time. This manifold, or fabric, is characterized by its inherent smoothness and contiguity. Even spatial movements across the cortex appear to alter the morphology of the FA curve (and therefore the TFA fabric) in a contiguous manner. Both excitation and inhibition contribute to pulling the FA curve away from an approximately 1/f baseline, effectively causing perturbations that determine its complex modulation. This interaction causes the spectral density curve to move in synchrony with the beat of the music, creating crests and troughs in the TFA fabric that show possible correlations with the rhythm and flow of the attended auditory stimuli. The effect appears to be more pronounced in the 30-40+ Hz region, where the vibrato of the voice, the texture of violins, and the hit of drums or cymbals appear to show up in prominent spectral amplitude crests (and sometimes troughs). This study suggests that the brain's oscillatory activity could be organized around (spectral) amplitude modulation. If so, the mechanism may extend beyond music, potentially playing a broader role in the encoding of attention.

In this paper I propose an approach to the development of technology for the analysis of electroacoustic music, which is referred to as the neurotechnology approach. I propose the unprecedented possibility of developing tools for the analysis of electroacoustic music based on neurophysiologic models of our auditory system. These tools would reveal the representations that our brain produces at various stages of the auditory pathway, from the cochlea to the cortex. Firstly I describe the journey that sounds take through the auditory pathways from the ears up to the auditory cortices and then I enumerate the sorts of tools that could be developed based on the various stages of this journey and the types of information that they would elicit.

Can we hear the sound of our brain? Is there any technique which can enable us to hear the neuro-electrical impulses originating from the different lobes of brain? The answer to all these questions is YES. In this paper we present a novel method with which we can sonify the Electroencephalogram (EEG) data recorded in rest state as well as under the influence of a simplest acoustical stimuli-a tanpura drone. The tanpura drone has a very simple yet very complex acoustic features, which is generally used for creation of an ambiance during a musical performance. Hence, for this pilot project we chose to study the correlation between a simple acoustic stimuli (tanpura drone) and sonified EEG data. Till date, there have been no study which deals with the direct correlation between a bio-signal and its acoustic counterpart and how that correlation varies under the influence of different types of stimuli. This is the first of its kind study which bridges this gap and looks for a direct correlation between music signal and EEG data using a robust mathematical microscope called Multifractal Detrended Cross Correlation Analysis (MFDXA). For this, we took EEG data of 10 participants in 2 min 'rest state' (i.e. with white noise) and in 2 min 'tanpura drone' (musical stimulus) listening condition. Next, the EEG signals from different electrodes were sonified and MFDXA technique was used to assess the degree of correlation (or the cross correlation coefficient γ x) between tanpura signal and EEG signals. The variation of γ x for different lobes during the course of the experiment also provides major interesting new information. Only music stimuli has the ability to engage several areas of the brain significantly unlike other stimuli (which engages specific domains only).

Three complementary methods are used to analyze the dynamics of multivariate EEG data obtained from a human listening to a piece of music. The analysis yields parameters for a data sonification that conserves temporal and frequency relationships as well as wave intensities of the data. Multiple events taking place on different time scales are combined to a polyrhythmic display in real time.

Working memory is the system that underlies the temporary storage and manipulation of information. However, the exact biological basis and neural mechanisms of cognitive function and working memory has yet to be fully uncovered. The temporary storage of information has been correlated with enhanced spiking activity in the form of persistent reverbaration for several seconds, as shown in vivo experiments. Several computational models have been designed to help our understanding of the conditions under which persistent activity can be sustained in cortical circuits. Here, we will focus on one such model that has shown promise, that uses polychronisation and short term synaptic dynamics to achieve this reverberation, and explore it with respect to different physiological parameters in the brain, including size of the network, number of synaptic connections, small-world connectivity, maximum axonal conduction delays, and type of cells (excitatory or inhibitory). We show that excitation and axonal conduction delays greatly affect the sustainability of spatio-temporal patterns of spikes called polychronous groups, even though the large variance in some of our results suggest that further exploration is needed on a larger scale of neuronal populations.

Spiking neural networks (SNNs) are considered to be more biologically realistic compared to typical rate-coded networks as they can model closely different types of neurons and their temporal dynamics. Typical spiking models use a number of fixed parameters such as the ratio between excitatory and inhibitory neurons. However, the parameters that are used in these models focus almost exclusively on our understanding of the neocortex with, for example, 80% of neurons chosen as excitatory and 20% inhibitory. In this paper we will evaluate how varying the ratio of excitatory versus inhibitory neurons, axonal conduction delays and the number of synaptic connections affect a SNN model by observing the change in mean firing rate and polychronization. Our main focus is to examine the effect on the emergence of spatiotemporal time-locked patterns, known as polychronous groups (PNGs). We show that the number of PNGs varies dramatically with a changing proportion of inhibitory neurons, that they increase exponentially as the number of synaptic connections is increased and that they decrease as the maximum axonal delays in the network increases. Our findings show that if we are to use SNNs and PNGs to model cognitive functions we must take into account these critical parameters.

The Fragmented Orchestra is a distributed musical instru-ment which combines live audio streams from geographi-cally disparate sites, and granulates each according to the spike timings of an artificial spiking neural network. This paper introduces the work, outlining its historical context, technical architecture, neuronal model and network infras-tructure, making specific reference to modes of interaction with the public.

This paper reports on the development of a proof-of-concept brain-computer music interfacing system (BCMI), which we built to be tested with a patient with Locked-in Syndrome at the Royal Hospital for Neuro-disability, in London. The system uses the Steady State Visual Evoked Potential (SSVEP) method, whereby targets are presented to a user on a computer monitor representing actions available to perform with the system. Each target is encoded by a flashing visual pattern reversing at a unique frequency. In order to make a selection, the user must direct her gaze at the target corresponding to the action she would like to perform. The patient grasped the concept quickly and rapidly demonstrated her skill at controlling the system with minimal practice. She was able to vary the intensity of her gaze, thus changing the amplitude of her EEG and vary the consequent musical parameters. We have proved the concept that such BCMI system is cost-effective to build, viable, and useful. However, ergonomic and design aspects of the system require further refinement in order to make it more practical for clinical usage. For instance, the system at present requires a therapist to place individual electrodes and calibrate a user's response to each stimulus, which can be time consuming. A new version of the system will require just positioning of a headset and, due to advanced algorithms, will require no calibration.

This paper presents a musical instrument, which uses in vitro neuronal networks to synthesise sounds. Cultures of dissociated neurons are grown on a dish with an embedded rectangular array of electrodes (MEA). Isolated neurons reconnect with one another via an extensive network of synaptically connected projections to form a dense monolayer of neurons. Cultures are provided with stimulation, which influences their activity, and can modify the culture's state. The core of the sound synthesis engine of our musical ...

The Fragmented Orchestra is a distributed musical instru- ment which combines live audio streams from geographi- cally disparate sites, and granulates each according to the spike timings of an artificial spiking neural network. This paper introduces the work, outlining its historical context, technical architecture, neuronal model and network infras- tructure, making specific reference to modes of interaction with the public.

This paper reports on the development of a proof-of-concept brain-computer music interfacing system (BCMI), which we built to be tested with a patient with Locked-in Syndrome at the Royal Hospital for Neuro-disability, in London. The system uses the Steady State Visual Evoked Potential (SSVEP) method, whereby targets are presented to a user on a computer monitor representing actions available to perform with the system. Each target is encoded by a flashing visual pattern reversing at a unique frequency. In order to make a selection, the user must direct her gaze at the target corresponding to the action she would like to perform. The patient grasped the concept quickly and rapidly demonstrated her skill at controlling the system with minimal practice. She was able to vary the intensity of her gaze, thus changing the amplitude of her EEG and vary the consequent musical parameters. We have proved the concept that such BCMI system is cost-effective to build, viable, and useful. However, ergonomic and design aspects of the system require further refinement in order to make it more practical for clinical usage. For instance, the system at present requires a therapist to place individual electrodes and calibrate a user's response to each stimulus, which can be time consuming. A new version of the system will require just positioning of a headset and, due to advanced algorithms, will require no calibration.

This paper describes early experiments, which attempt to reconfigure the sound of a breath using a network of artificial spiking cortical neurons. The connectivity of the network evolves according to a spike timing dependent plasticity algorithm and the instrument triggers grains of sound from recordings of the breath when any of the neurons fire.

The Fragmented Orchestra is a distributed musical instru-ment which combines live audio streams from geographi-cally disparate sites, and granulates each according to the spike timings of an artificial spiking neural network. This paper introduces the work, outlining its historical context, technical architecture, neuronal model and network infras-tructure, making specific reference to modes of interaction with the public.

The human brain is a remarkable computing machine, i.e. vastly parallel, self-organizing, robust, and energy efficient. To gain a better understanding into how the brain works, a cyborg (cybernetic organism, a combination of machine and living tissue) is currently being made in an interdisciplinary effort, known as the Cyborg project. In this paper we describe how living cultures of neurons (biological neural networks) are successfully grown in-vitro over Micro-Electrode Arrays (MEAs), which allow them to be interfaced to a robotic body through electrical stimulation and neural recordings. Furthermore, we describe the bio-and nano-technological procedures utilized for the culture of such dissociated neural networks and the interface software and hardware framework used for creating a closed-loop hybrid neuro-system. A Reservoir Computing (RC) approach is used to harness the computational power of the neuronal culture.

We present a new technique for granular sampling using a pulsecoupled network of spiking artificial neurons to generate grain events. The system plays randomly selected sound grains from a given sound sample when any one of a weakly coupled network of up to 1000 neurons fires. The network can exhibit loosely correlated temporal solutions and also collective synchronised behaviour. This leads to very interesting sonic results, particularly with regard to rhythmic textures which can be controlled with various parameters within the model.

Can we hear the sound of our brain? Is there any technique which can enable us to hear the neuro-electrical impulses originating from the different lobes of brain? The answer to all these questions is YES. In this paper we present a novel method with which we can sonify the Electroencephalogram (EEG) data recorded in rest state as well as under the influence of a simplest acoustical stimuli-a tanpura drone. The tanpura drone has a very simple yet very complex acoustic features, which is generally used for creation of an ambiance during a musical performance. Hence, for this pilot project we chose to study the correlation between a simple acoustic stimuli (tanpura drone) and sonified EEG data. Till date, there have been no study which deals with the direct correlation between a bio-signal and its acoustic counterpart and how that correlation varies under the influence of different types of stimuli. This is the first of its kind study which bridges this gap and looks for a direct correlation between music signal and EEG data using a robust mathematical microscope called Multifractal Detrended Cross Correlation Analysis (MFDXA). For this, we took EEG data of 10 participants in 2 min 'rest state' (i.e. with white noise) and in 2 min 'tanpura drone' (musical stimulus) listening condition. Next, the EEG signals from different electrodes were sonified and MFDXA technique was used to assess the degree of correlation (or the cross correlation coefficient γ x) between tanpura signal and EEG signals. The variation of γ x for different lobes during the course of the experiment also provides major interesting new information. Only music stimuli has the ability to engage several areas of the brain significantly unlike other stimuli (which engages specific domains only).

Despite of the simplicity of their construction, cellular automata are capable of very complicated behavior with very little specification. The present paper introduces an algorithmic music system incorporating a voting rule type automaton extended with energy profiles and an elaborate mapping algorithm. One core design principle is the fusion of generative, implicit behavior with explicit user designed symbolic processes, in particular, facing the mapping task.

This paper describes the development, evaluation and use of the computer program ModusXXI, a music generator that can create a practically unlimited number of atonal melodic dictations, or melodic material for sight-reading, based on the Modus Novus methodology [Edlund, 1963]. Lars Edlund's Modus Novus organizes the aural study of atonal melody structure on the combinations of intervals that will break the bonds with any tonal context; a characteristic of some music composed since the first half of the 20th century. Each chapter has certain melodic material that has been grouped together, according to the musical intervals that they contain, in groups of increasing degree of difficulty. Although there are other atonal ear-training methods [Friedmann, 1990], Modus Novus is the only one known to us that concentrates specifically on the study of the melodic interval structure of atonal music. At present, several systems are available, or have been proposed, that generate music with computers. Nevertheless, none of these organizes the melodic structure upon specific atonal intervallic content. ModusXXI is a computer application with an easy-to-use graphic user interface, that runs on systems that support the Java Virtual Machine [www.java.com]. It generates melodies following the Modus Novus methodology, based on random selection of notes and rhythmic values. ModusXXI was tested with a group of music students, obtaining a Mean Opinion Score acceptance of over 80%.

This paper introduces the interactive and performative installation artwork, Plasticity (Jane Grant, John Matthias, Nick Ryan and Kin) and its software engine the Neurogranular Sampler via a journey through the synchronized pendulum clocks of Christian Huygens, entrainment in dynamical systems, and correlations and noise within neuronal networks. I examine ways in which the public are ‘playing with noise’ in the artwork and suggest that the public engagement with the work is closely connected to the fact that the dynamics of the artificial neuronal network lie at the borders between synchrony and randomness.

Since the turn of this century, a substantial body of research has been published on the neuroscience of creativity. Now, it is necessary to study the neurophysiological correlates in true-to-life, professionally specific situations. The aim of our empirical research was to study the neurophysiological correlates of musical improvisation, a spontaneous creative activity. The participants were 136 right-handed practicing musicians aged 19 to 36 (102 males and 34 females), divided into two groups-professionals (56 people) and amateurs (80 people). EEG signals were recorded in a resting state (eyes closed) and during three types of internal musical activity (perceiving, mentally reproducing, and mentally improvising) from 21 scalp electrodes according to the International 10-20 System. For statistical analysis, we used ANOVA and post hoc analysis. For the main neurophysiological correlates of musical creativity, we revealed higher values of EEG spectral power in the delta band and the dominance of long-distance functional cortical connections in the high-frequency bands. Variable neurophysiological correlates were differentiated according to emotions and the professional level of the musicians. The distinguishing EEG pattern in the professional musicians during improvisation was the predominant activation of the lefthemisphere cortical regions simultaneously with high interhemispheric integration in the high-frequency band along the "creativity axis. " The revealed neurophysiological correlates of musical creativity during improvisation included basic and variable components and were characterized by a specific frequency-spatial organization of bioelectric cortical activity in the musicians.

This paper brings together perspectives of the ICMC 2007 ArtAbilitation Panel on non-formal rehabilitation via immersive interactive music environments. Issues covered are sound therapy and music therapy, musical topologies, brainwave control and research methodology.

The study investigated the relative effectiveness of different proportions of time spent on physical and mental practice, in the context of a music performance of a tonal pattern over harmonic progressions of two difficulty levels. Using a sampling without replacement procedure, sixty undergraduate students were assigned to four practice groups partially blocked for musical instrument. The groups included a physical practice group, a mental practice group and two combined mental and physical practice groups in the proportions of (a) 66% physical and 33% mental, and (b) 33% physical and 66% mental. Each subject performed a pretest, a 3 minute practice session, and a posttest on both harmonic progressions. Presentation of the harmonic progressions were counterbalanced to control for practice effects All pre- and posttests were recorded and scored according to number of note errors. An ANCOVA procedure using pretest scores as covariates revealed that: (a) there were no differences betw...

One of the challenges of researching spiking neural networks (SNN) is translation from temporal spiking behavior to classic controller output. While many encoding schemes exist to facilitate this translation, there are few benchmarks for neural networks that inherently utilize a temporal controller. In this work, we consider the common reinforcement problem of animat locomotion in an environment suited for evaluating SNNs. Using this problem, we explore novel methods of reward distribution as they impacts learning. Hebbian learning, in the form of spike time dependent plasticity (STDP), is modulated by a dopamine signal and affected by reward-induced neural activity. Different reward strategies are parameterized and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is used to find the best strategies for fixed animat morphologies. The contribution of this work is twofold: to cast the problem of animat locomotion in a form directly applicable to simple temporal controllers, and to demonstrate novel methods for reward modulated Hebbian learning.

This paper presents a musical instrument, which uses in vitro neuronal networks to synthesise sounds. Cultures of dissociated neurons are grown on a dish with an embedded rectangular array of electrodes (MEA). Isolated neurons reconnect with one another via an extensive network of synaptically connected projections to form a dense monolayer of neurons. Cultures are provided with stimulation, which influences their activity, and can modify the culture’s state. The core of the sound synthesis engine of our musical instrument is a monophonic additive synthesizer using sinusoidal oscillators. We devised a method to generate frequencies, phase and amplitude values for the oscillators from the electrical activity of the neurons.

The Fragmented Orchestra is a distributed musical instru-ment which combines live audio streams from geographi-cally disparate sites, and granulates each according to the spike timings of an artificial spiking neural network. This paper introduces the work, outlining its historical context, technical architecture, neuronal model and network infras-tructure, making specific reference to modes of interaction with the public.

In this paper we investigate the effect of biasing the axonal connection delay values in the number of polychronous groups produced for a spiking neuron network model. We use an estimation of distribution algorithm (EDA) that learns tree models to search for optimal delay configurations. Our results indicate that the introduced approach can be used to considerably increase the number of such groups.

This paper presents a musical instrument, which uses in vitro neuronal networks to synthesise sounds. Cultures of dissociated neurons are grown on a dish with an embedded rectangular array of electrodes (MEA). Isolated neurons reconnect with one another via an extensive network of synaptically connected projections to form a dense monolayer of neurons. Cultures are provided with stimulation, which influences their activity, and can modify the culture’s state. The core of the sound synthesis engine of our musical instrument is a monophonic additive synthesizer using sinusoidal oscillators. We devised a method to generate frequencies, phase and amplitude values for the oscillators from the electrical activity of the neurons.

This chapter elaborates on sonification as an experimental method. It is based on the premise that there is no such thing as unconditional insight, no isolated discovery or invention; all research depends on methods. The understanding of their correct functioning depends on the context. Sonification as a relatively new ensemble of methods therefore requires the re-thinking and re-learning of commonly embraced understandings; a process that requires much experimentation.

Musicians and composers have been using brainwaves as generative sources in music for at least 40 years and the possibility of a brain-computer interface for direct communication and control was first seriously investigated in the early 1970s. Work has been done by many artists and technologists in the intervening years to attempt to control music systems with brainwaves and - indeed - many other biological signals. Despite the richness of EEG, fMRI and other data which can be read from the human brain, there has up to now been only limited success in translating the complex encephalographic data into satisfactory musical results. We are currently pursuing research which we believe will lead to the possibility of direct brain-computer interfaces for rich and expressive musical control. This report will outline the directions of our current research and results.

This paper describes early experiments, which attempt to reconfigure the sound of a breath using a network of artificial spiking cortical neurons. The connectivity of the network evolves according to a spike timing dependent plasticity algorithm and the instrument triggers grains of sound from recordings of the breath when any of the neurons fire.

The Fragmented Orchestra is a distributed musical instrument which combines live audio streams from geographically disparate sites, and granulates each according to thespike timings of an artificial spiking neural network. Thispaper introduces the work, outlining its historical context,technical architecture, neuronal model and network infrastructure, making specific reference to modes of interactionwith the public.

We introduce a method of generating grain parameters of a granular synthesiser in real-time by using a network of artificial spiking neurons, the behaviour of which is determined by user-control of a small number of network parameters; 'Neurogranular synthesis'. The artificial network can exhibit a wide variety of behaviour from loosely correlated to highly synchronised, which can produce interesting sonic results, particularly with regard to rhythmic textures.

Music Neurotechnology is a new research area that is emerging at the crossroads of Neurobiology, Engineering Sciences and Music. Examples of ongoing research into this new area include the development of brain-computer interfaces to control music systems and systems for automatic classification of sounds informed by the neurobiology of the human auditory apparatus. In this paper we introduce neurogranular sampling, a new sound synthesis technique based on spiking neuronal networks (SNN). We have implemented a neurogranular sampler using the SNN model developed by Izhikevich, which reproduces spiking and bursting behavior of known types of cortical neurons. The neurogranular sampler works by taking short segments (or sound grains) from sound files and triggering them when any of the neurons fire.

We present a new technique for granular sampling using a pulsecoupled network of spiking artificial neurons to generate grain events. The system plays randomly selected sound grains from a given sound sample when any one of a weakly coupled network of up to 1000 neurons fires. The network can exhibit loosely correlated temporal solutions and also collective synchronised behaviour. This leads to very interesting sonic results, particularly with regard to rhythmic textures which can be controlled with various parameters within the model.

In this paper, a new decisively important factor in both the perceptual and the automated piano-guitar identification process is introduced. This factor is determined by the nontonal spectral content of a note, while it is, in practice, totally independent of the note spectrum tonal part. This conclusion and all related results are based on a number of extended acoustical experiments, performed over the full pitch range of each instrument. The notes have been recorded from six different performers each of whom played a different instrument. Next, a number of powerful criteria for the classification between guitar and piano is proposed. Using these criteria, automated classification between 754 piano and guitar test notes has been achieved with a 100% success rate.

Methods for spectral analysis of audio signals and their graphical display are widespread. However, assessing mu- sic and audio in the visual domain involves a number of challenges in the translation between auditory images into mental or symbolically represented concepts. This paper presents a spectral analysis method that exists entirely in the auditory domain, and results in an auditory presenta- tion of a spectrum. It aims to strip a segment of audio sig- nal of its temporal content, resulting in a quasi-stationary signal that possesses a similar spectrum to the original sig- nal. The method is extended and applied for the purpose of music summarisation.

Methods for spectral analysis of audio signals and their graphical display are widespread. However, assessing music and audio in the visual domain involves a number of challenges in the translation between auditory images into mental or symbolically represented concepts. This paper presents a spectral analysis method that exists entirely in the auditory domain, and results in an auditory presentation of a spectrum. It aims to strip a segment of audio signal of its temporal content, resulting in a quasi-stationary signal that possesses a similar spectrum to the original signal. The method is extended and applied for the purpose of music summarisation.

To understand and analysis behavior of complicated and intelligent organisms, scientists apply bio-inspired concepts including evolution and learning to mathematical models and analyses. Researchers utilize these perceptions in different applications, searching for improved methods and approaches for modern computational systems. This paper presents a genetic algorithm based evolution framework in which Spiking Neural Network (SNN) of artificial creatures are evolved for higher chance of survival in a virtual environment. The artificial creatures are composed of randomly connected Izhikevich spiking reservoir neural networks using population activity rate coding. Inspired by biological neurons, the neuronal connections are considered with different axonal conduction delays. Simulations results prove that the evolutionary algorithm has the capability to find or synthesis artificial creatures which can survive in the environment successfully.

Neurons in central nervous system process information in networks formed by collection of neurons. Such networks perform computations in the brain resulting in decision making and other controlling signals to be generated by brain. Multi Electrode array dish is used for studying such network. Disassociated cultures are used to study the network topology and record the electrical activity occurring in the network of neurons. Electrical signal obtained from the various nodes of neural network are analysed to comment upon the diverseness in network information. Diversity index is used here. The quantification of diverseness is done in the units of bit.

The use of hybrid technologies creates the opportunity to combine nano-scale technologies, microelectronics and neuroscience in order to build devices for non-invasive stimulation and recording of cultured neural cells at nano-level. A hybrid neuroelectronic platfjorm Jbr neuroprocessing is proposed as a means to develop a new intelligent injbrmation system biologically inspired.