Identification of Musical Instruments by means of the Hough-Transformation

In order to distinguish between the sounds of different musical instruments, certain instrument-specific sound features have to be extracted from the time series representing a given recorded sound. The Hough Transform is a pattern recognition procedure that is usually applied to detect specific curves or shapes in digital pictures (Shapiro, 1978). Due to some similarity between pattern recognition and statistical curve fitting problems, it may as well be applied to sound data (as a special case of time series data). The transformation is parameterized to detect sinusoidal curve sections in a digitized sound, the motivation being that certain sounds might be identified by certain oscillation patterns. The returned (transformed) data is the timepoints and amplitudes of detected sinusoids, so the result of the transformation is another ?condensed? time series. This specific Hough Transform is then applied to sounds played by different musical instruments. The generated data is investigated for features that are specific for the musical instrument that played the sound. Several classification methods are tried out to distinguish between the instruments and it turns out that RDA (a hybrid method combining LDA and QDA) (Friedman, 1989) performs best. The resulting error rate is better than those achieved by humans (Bruderer, 2003). --

Parallel Quantum Hough Transform

Few of the known quantum algorithms can be reliably executed on a quantum computer. Therefore, as an extension, we propose a Parallel Quantum Hough transform (PQHT) algorithm that we execute on a quantum computer. We give its implementation and discuss the results obtained. The PQHT algorithm is conceptually divided into a parallel rotation stage consisting of a set of connected programmable $\texttt{RZ}$ rotation gates, with adjustable node connections of coincidence detectors realized with quantum logic gates. The modules were developed using IBM Quantum Composer and tested using the IBM QASM simulator. Finally, the modules were programmed using the Python package Qiskit and the jobs were sent to distributed IBM Q System One quantum computers. The successful run results on Fraunhofer Q System One in Ehningen will be presented as a proof of concept for the PQHT algorithm.Comment: 7 pages, 4 figure

Periodicity pitch perception part III: sensibility and Pachinko volatility

Neuromorphic computer models are used to explain sensory perceptions. Auditory models generate cochleagrams, which resemble the spike distributions in the auditory nerve. Neuron ensembles along the auditory pathway transform sensory inputs step by step and at the end pitch is represented in auditory categorical spaces. In two previous articles in the series on periodicity pitch perception an extended auditory model had been successfully used for explaining periodicity pitch proved for various musical instrument generated tones and sung vowels. In this third part in the series the focus is on octopus cells as they are central sensitivity elements in auditory cognition processes. A powerful numerical model had been devised, in which auditory nerve fibers (ANFs) spike events are the inputs, triggering the impulse responses of the octopus cells. Efficient algorithms are developed and demonstrated to explain the behavior of octopus cells with a focus on a simple event-based hardware implementation of a layer of octopus neurons. The main finding is, that an octopus' cell model in a local receptive field fine-tunes to a specific trajectory by a spike-timing-dependent plasticity (STDP) learning rule with synaptic pre-activation and the dendritic back-propagating signal as post condition. Successful learning explains away the teacher and there is thus no need for a temporally precise control of plasticity that distinguishes between learning and retrieval phases. Pitch learning is cascaded: At first octopus cells respond individually by self-adjustment to specific trajectories in their local receptive fields, then unions of octopus cells are collectively learned for pitch discrimination. Pitch estimation by inter-spike intervals is shown exemplary using two input scenarios: a simple sinus tone and a sung vowel. The model evaluation indicates an improvement in pitch estimation on a fixed time-scale

VLSI Implementierung eines parallelen Hough-Transformations-Prozessors mit dynamisch nachladbaren Mustern

In 1.0 5m CMOS Technik wurde ein Prozessor zur parallelen Verarbeitung einer speziellen Hough-Transformation entwickelt. Bei der auf 50 MHz ausgelegten Taktfrequenz koennen 6.4 x 10E+10 Objektmuster pro Sekunde detektiert werden. Bis zu 5 x 10E+7 zu detektierende Suchmuster koennen pro Sekunde in den Prozessor geladen werden. Damit koennen erstmals Echtzeitapplikationen in der Bildverarbeitung im Mikrosekundenbereich erschlossen werden

Modeling the formation process of grouping stimuli sets through cortical columns and microcircuits to feature neurons

A computational model of a self-structuring neuronal net is presented in which repetitively applied pattern sets induce the formation of cortical columns and microcircuits which decode distinct patterns after a learning phase. In a case study, it is demonstrated how specific neurons in a feature classifier layer become orientation selective if they receive bar patterns of different slopes from an input layer. The input layer is mapped and intertwined by self-evolving neuronal microcircuits to the feature classifier layer. In this topical overview, several models are discussed which indicate that the net formation converges in its functionality to a mathematical transform which maps the input pattern space to a feature representing output space. The self-learning of the mathematical transform is discussed and its implications are interpreted. Model assumptions are deduced which serve as a guide to apply model derived repetitive stimuli pattern sets to in vitro cultures of neuron ensembles to condition them to learn and execute a mathematical transform

ENABLE - A Systolic 2nd Level Trigger Processor for Track Finding and e/p Discrimination for ATLAS/LHC

The Enable Machine is a systolic 2nd level trigger processor for the transition radiation detector (TRD) of ATLAS/LHC. It is developed within the EAST/RD-11 collaboration at CERN. The task of the processor is to find electron tracks and to reject pion tracks according to the EAST benchmark algorithm in less than 10 ms. Track are identified by template matching in a (f,z) region of interest (RoI) selected by a 1st level trigger. In the (f,z) plane tracks of constant curvature are straight lines. The relevant lines form mask templates. Track identification is done by histogramming the coincidences of the templates and the RoI data for each possible track. The Enable Machine is an array processor that handles tracks of the same slope in parallel, and tracks of different slope in a pipeline. It is composed of two units, the Enable histogrammer unit and the Enable z/f-board. The interface daughter board is equipped with a HIPPI-interface developed at JINR/Dubna, and Xilinx 'corner turning' data converter chips. Enable uses programmable gate arrays (XILINX) for histogramming and synchronous SRAMs for pattern storage. With a clock rate of 40 MHz the trigger decision time is 6.5 ms and the latency 7.0 ms. The Enable machine is scalable in the RoI size as well as in the number of tracks processed. It can be adapted to different recognition tasks and detector setups. The prototype of the Enable Machine has been tested in a beam time of the RD6 collaboration at CERN in October 1993

Results of On-Line Tests of the ENABLE Prototype, a 2nd Level Trigger Processor for the TRT of ATLAS/LHC

The Enable Machine is a systolic 2nd level trigger processor for the transition radiation detector (TRD) of ATLAS/LHC. The task of the processor is to find the best candidate for a lepton track in a high background of pions according to the EAST benchmark [2] in less than 10 5s. As described earlier [1, 2] the Enable Machine finds all reasonable tracks by histogramming the coincidence of the mask templates and the RoI for each track. A prototype has been developed and tested within the EAST/RD-11 collaboration at CERN. It operates at 50 MHz and finds up to 400 arbitrary tracks in less than 10 5s. It is assembled of an interface board and one or more histogrammer boards which makes the Enable Machine easily scalable. The histogrammer units are systolic arrays consisting of a matrix of 36 field- programmable gate arrays (Xilinx XC3190). Through this it is possible to optimize the trigger algorithm, to adapt it to a changed detector setup, and it allows even the implementation of completly new algorithms. For the beam tests in autumn 1993 at CERN the overall functionality within the detector environment could be shown. We were able to link successfully the Enable prototype to the detector raw data stream as well as to the data acquisition system. For the next beam period in 1994 we will focus on efficiency measurements and tests with maximal detector data rate

Modeling Pitch Perception With an Active Auditory Model Extended by Octopus Cells

Pitch is an essential category for musical sensations. Models of pitch perception are vividly discussed up to date. Most of them rely on definitions of mathematical methods in the spectral or temporal domain. Our proposed pitch perception model is composed of an active auditory model extended by octopus cells. The active auditory model is the same as used in the Stimulation based on Auditory Modeling (SAM), a successful cochlear implant sound processing strategy extended here by modeling the functional behavior of the octopus cells in the ventral cochlear nucleus and by modeling their connections to the auditory nerve fibers (ANFs). The neurophysiological parameterization of the extended model is fully described in the time domain. The model is based on latency-phase en- and decoding as octopus cells are latency-phase rectifiers in their local receptive fields. Pitch is ubiquitously represented by cascaded firing sweeps of octopus cells. Based on the firing patterns of octopus cells, inter-spike interval histograms can be aggregated, in which the place of the global maximum is assumed to encode the pitch

Programmable active memories in real-time tasks: implementing data-driven triggers for LHC experiments

The future Large Hadron Collider (LHC), to be built at CERN, presents among other technological challenges a formidable problem of real-time data analysis. At a primary event rate of 40 MHz, a multi-stage trigger system has to analyze data to decide which is the fraction of events that should be preserved on permanent storage for further analysis. We report on implementations of local algorithms for feature extraction as part of triggering, using the detectors of the proposed ATLAS experiment as a model. The algorithms were implemented for a decision frequency of 100 kHz, on different data-driven programmable devices based on structures of field- programmable gate arrays and memories. The implementations were demonstrated at full speed with emulated input, and were also integrated into a prototype detector running in a test beam at CERN, in June 1994

A Systolic Track Finding Trigger Processor

This paper outlines the architecture of a systolic trigger processor for tracking detectors of the jetchamber type, operated inside a solenoidal magnetic field. It outputs the number of tracks and their radius of curvature in less than 10 µs. A detailed description of the constant data flow scheme is given which is maintained thoroughly from the read-out of the drift times up to the final storage of the results in registers and the creation of the trigger signal. The system consists of a preprocessor for timing signals, a Hough transform processor that determines well defined tracks, and an Euler processor that counts their number. This is done by applying the Euler relation to a binary image which is obtained from the Hough transform result by using a suitable threshold. All system components can be partitioned and integrated in field programmable gate arrays. 1. Introduction For tracking detectors at storage rings a trigger is required which outputs the number of tracks and their r..