A Hardware Based Audio Event Detection System

Audio event detection and analysis is an important tool in many fields, from entertainment to security. Recognition technologies are used daily for parsing voice commands, tagging songs, and real time detection of crimes or other undesirable events. The system described in this work is a hardware based application of an audio detection system, implemented on an FPGA. It allows for the detection and characterization of gunshots and other events, such as breaking glass, by comparing a recorded audio sample to 20+ stored fingerprints in real time. Additionally, it has the ability to record flagged events and supports integration with mesh networks to send alerts

Design of Pattern Matching Systems: Pattern, Algorithm, and Scanner

Pattern matching is at the core of many computational problems, e.g., search engine, data mining, network security and information retrieval. In this dissertation, we target at the more complex patterns of regular expression and time series, and proposed a general modular structure, named character class with constraint repetition (CCR), as the building block for the pattern matching algorithm. An exact matching algorithm named MIN-MAX is developed to support overlapped matching of CCR based regexps, and an approximate matching algorithm named Elastic Matching Algorithm is designed to support overlapped matching of CCR based time series, i.e., music melody. Both algorithms are parallelized to run on FPGA to achieve high performance, and the FPGA-based scanners are designed as a modular architecture which is parameterizable and can be reconfigured by simple memory writes, achieving a perfect balance between performance and deployment time

String Matching Problems with Parallel Approaches An Evaluation for the Most Recent Studies

In recent years string matching plays a functional role in many application like information retrieval, gene analysis, pattern recognition, linguistics, bioinformatics etc. For understanding the functional requirements of string matching algorithms, we surveyed the real time parallel string matching patterns to handle the current trends. Primarily, in this paper, we focus on present developments of parallel string matching, and the central ideas of the algorithms and their complexities. We present the performance of the different algorithms and their effectiveness. Finally this analysis helps the researchers to develop the better techniques

Digital neuromorphic auditory systems

This dissertation presents several digital neuromorphic auditory systems. Neuromorphic systems are capable of running in real-time at a smaller computing cost and consume lower power than on widely available general computers. These auditory systems are considered neuromorphic as they are modelled after computational models of the mammalian auditory pathway and are capable of running on digital hardware, or more specifically on a field-programmable gate array (FPGA). The models introduced are categorised into three parts: a cochlear model, an auditory pitch model, and a functional primary auditory cortical (A1) model. The cochlear model is the primary interface of an input sound signal and transmits the 2D time-frequency representation of the sound to the pitch models as well as to the A1 model. In the pitch model, pitch information is extracted from the sound signal in the form of a fundamental frequency. From the A1 model, timbre information in the form of time-frequency envelope information of the sound signal is extracted. Since the computational auditory models mentioned above are required to be implemented on FPGAs that possess fewer computational resources than general-purpose computers, the algorithms in the models are optimised so that they fit on a single FPGA. The optimisation includes using simplified hardware-implementable signal processing algorithms. Computational resource information of each model on FPGA is extracted to understand the minimum computational resources required to run each model. This information includes the quantity of logic modules, register quantity utilised, and power consumption. Similarity comparisons are also made between the output responses of the computational auditory models on software and hardware using pure tones, chirp signals, frequency-modulated signal, moving ripple signals, and musical signals as input. The limitation of the responses of the models to musical signals at multiple intensity levels is also presented along with the use of an automatic gain control algorithm to alleviate such limitations. With real-world musical signals as their inputs, the responses of the models are also tested using classifiers – the response of the auditory pitch model is used for the classification of monophonic musical notes, and the response of the A1 model is used for the classification of musical instruments with their respective monophonic signals. Classification accuracy results are shown for model output responses on both software and hardware. With the hardware implementable auditory pitch model, the classification score stands at 100% accuracy for musical notes from the 4th and 5th octaves containing 24 classes of notes. With the hardware implementation auditory timbre model, the classification score is 92% accuracy for 12 classes musical instruments. Also presented is the difference in memory requirements of the model output responses on both software and hardware – pitch and timbre responses used for the classification exercises use 24 and 2 times less memory space for hardware than software

Universal Event and Motion Editor for Robots\u27 Theatre

Most of work on motion of mobile robots is to generate plans for avoiding obstacles or perform some meaningful and useful actions. In modern robot theatres and entertainment robots the motions of the robot are scripted and thus the performance or behavior of the robot is always the same. In this work we want to propose a new approach to robot motion generation. We want our robot to behave more like real people. People do not move in mechanical way like robots. When a human is supposed to execute some motion, these motions are similar to one another but always slightly or not so slightly different. We want to reproduce this property based on the introduced by us new concept of probabilistic regular expression, a method to describe sets of interrelated similar actions instead of single actions. Our goal is not only to create motions for humanoid robots that will look more naturally and less mechanically, but also to program robots that will combine basic movements from certain library in many different and partially random ways. While the basic motions were created ahead of time, their combinations are specified in our new language. Although now our method is only for motions and does not take inputs from sensors into account, in future the language can be extended to input/output sequences, thus the robot will be able to adapt the motion in different ways, to some sets of sequences of input stimuli. The inputs will come from sensors, possibly attached to limbs of controlling humans from whom the patterns of motion will be acquired

Proceedings of the 19th Sound and Music Computing Conference

Proceedings of the 19th Sound and Music Computing Conference - June 5-12, 2022 - Saint-Étienne (France). https://smc22.grame.f

16th Sound and Music Computing Conference SMC 2019 (28–31 May 2019, Malaga, Spain)

The 16th Sound and Music Computing Conference (SMC 2019) took place in Malaga, Spain, 28-31 May 2019 and it was organized by the Application of Information and Communication Technologies Research group (ATIC) of the University of Malaga (UMA). The SMC 2019 associated Summer School took place 25-28 May 2019. The First International Day of Women in Inclusive Engineering, Sound and Music Computing Research (WiSMC 2019) took place on 28 May 2019. The SMC 2019 TOPICS OF INTEREST included a wide selection of topics related to acoustics, psychoacoustics, music, technology for music, audio analysis, musicology, sonification, music games, machine learning, serious games, immersive audio, sound synthesis, etc

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition