Wireless audio networking modifying the IEEE 802.11 standard to handle multi-channel real-time wireless audio networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityAudio networking is a rapidly increasing field which introduces new exiting possibilities for the professional audio industry. When well established, it will drastically change the way live sound systems will be designed, built and used. Today's networks have enough bandwidth that enables them to transfer hundreds of high quality audio channels, replacing analogue cables and intricate installations of conventional analogue audio systems. Currently there are many systems in the market that distribute audio over networks for live music and studio applications, but this technology is not yet widespread. The reasons that audio networks are not as popular as it was expected are mainly the lack of interoperability between different vendors and still, the need of a wired network infrastructure. Therefore, the development of a wireless digital audio networking system based on the existing widespread wireless technology is a major research challenge. However, the ΙΕΕΕ 802.11 standard, which is the primary wireless networking technology today, appears to be unable to handle this type of application despite the large bandwidth available. Apart from the well-known drawbacks of interference and security, encountered in all wireless data transmission systems, the way that ΙΕΕΕ 802.11 arbitrates the wireless channel access causes significantly high collision rate, low throughput and long overall delay. The aim of this research was to identify the causes that impede this technology to support real time wireless audio networks and to propose possible solutions. Initially the standard was tested thoroughly using a data traffic model which emulates a multi-channel real time audio environment. Broadcasting was found to be the optimal communication method, in order to satisfy the intolerance of live audio, when it comes to delay. The results were analysed and the drawback was identified in the hereditary weakness of the IEEE 802.11 standard to manage broadcasting, from multiple sources in the same network. To resolve this, a series of modifications was proposed for the Medium Access Control algorithm of the standard. First, the extended use of the "CTS-to-Self" control message was introduced in order to act as a protection mechanism in broadcasting, similar to the RTC/CTS protection mechanism, already used in unicast transmission. Then, an alternative "random backoff" method was proposed taking into account the characteristics of live audio wireless networks. For this method a novel "Exclusive Backoff Number Allocation" (EBNA) algorithm was designed aiming to minimize collisions. The results showed that significant improvement in throughput can be achieved using the above modifications but further improvement was needed, when it comes to delay, in order to reach the internationally accepted standards for real time audio delivery. Thus, a traffic adaptive version of the EBNA algorithm was designed. This algorithm monitors the traffic in the network, calculates the probability of collision and accordingly switches between classic IEEE 802.11 MAC and EBNA which is applied only between active stations, rather than to all stations in the network. All amendments were designed to operate as an alternative mode of the existing technology rather as an independent proprietary system. For this reason interoperability with classic IEEE 802.11 was also tested and analysed at the last part of this research. The results showed that the IEEE 802.11 standard, suitably modified, is able to support multiple broadcasting transmission and therefore it can be the platform upon which, the future wireless audio networks will be developed

A modified IEEE 802.11 MAC for optimizing broadcasting in wireless audio networks

The use of network infrastructures to replace conventional professional audio systems is a rapidly increasing field which is expected to play an important role within the professional audio industry. Currently, the market is dominated by numerous proprietary protocols which does not allowing interoperability and does not promote the evolution on this sector. Recent standardization actions are intending to resolve this issue excluding however the use of wireless networks. Existing wireless networking technologies are considered unsuitable for supporting real-time audio networks, not because of lack of bandwidth but due to their inefficient congestion control mechanisms in broadcasting. In this paper, we propose an amendment of the IEEE 802.11 MAC that improves the performance of the standard when is used for real-time audio data delivery. The proposed amendment is based on two innovative ideas. First, it provides a protection mechanism for broadcasting and second, replaces the classic congestion control mechanism, based in random backoff, with an alternative traffic adaptive algorithm, designed to minimize collisions. The proposed MAC is able to operate as an alternative mode, allowing regular Wi-Fi networks to coexist and interoperate efficiently with audio networks, with the last ones being able to be deployed over existing wireless network infrastructures

Electroglottography based real-time voice-to-MIDI controller

Voice-to-MIDI real-time conversion is a challenging problem that comes with a series of obstacles and complications. The main issue is the tracking of the human voice pitch. Extracting the voice fundamental frequency can be inaccurate and highly computationally exacting due to the spectral complexity of voice signals. In addition, on account of microphone usage, the presence of environmental noise can further affect voice processing. An analysis of the current research and status of the market shows a plethora of voice-to-MIDI implementations revolving around the processing of audio signals deriving from microphones. This paper addresses the above-mentioned issues by implementing a novel experimental method where electroglottography is employed instead of microphones as a source for pitch-tracking. In the proposed system, the signal is processed and converted through an embedded hardware device. The use of electroglottography improves both the accuracy of pitch evaluation and the ease of voice information processing; firstly, it provides a direct measurement of the vocal folds' activity and, secondly, it bypasses the interferences caused by external sound sources. This allows the extraction of a simpler and cleaner signal that yields a more effective evaluation of the fundamental frequency during phonation. The proposed method delivers a faster and less computationally demanding conversion thus in turn, allowing for an efficacious real-time voice-to-MIDI conversion

Schema theory based data engineering in gene expression programming for big data analytics

Gene expression programming (GEP) is a data driven evolutionary technique that well suits for correlation mining. Parallel GEPs are proposed to speed up the evolution process using a cluster of computers or a computer with multiple CPU cores. However, the generation structure of chromosomes and the size of input data are two issues that tend to be neglected when speeding up GEP in evolution. To fill the research gap, this paper proposes three guiding principles to elaborate the computation nature of GEP in evolution based on an analysis of GEP schema theory. As a result, a novel data engineered GEP is developed which follows closely the generation structure of chromosomes in parallelization and considers the input data size in segmentation. Experimental results on two data sets with complementary features show that the data engineered GEP speeds up the evolution process significantly without loss of accuracy in data correlation mining. Based on the experimental tests, a computation model of the data engineered GEP is further developed to demonstrate its high scalability in dealing with potential big data using a large number of CPU cores

Classification of Speaking and Singing Voices Using Bioimpedance Measurements and Deep Learning

The acts of speaking and singing are different phenomena displaying distinct characteristics. The classification and distinction of these voice acts is vastly approached utilizing voice audio recordings and microphones. The use of audio recordings, however, can become challenging and computationally expensive due to the complexity of the voice signal. The research presented in this paper seeks to address this issue by implementing a deep learning classifier of speaking and singing voices based on bioimpedance measurement in replacement of audio recordings. In addition, the proposed research aims to develop a real-time voice act classification for the integration with voice-to-MIDI conversion. For such purposes, a system was designed, implemented, and tested using electroglottographic signals, Mel Frequency Cepstral Coefficients, and a deep neural network. The lack of datasets for the training of the model was tackled by creating a dedicated dataset 7200 bioimpedance measurement of both singing and speaking. The use of bioimpedance measurements allows to deliver high classification accuracy whilst keeping low computational needs for both preprocessing and classification. These characteristics, in turn, allows a fast deployment of the system for near-real-time applications. After the training, the system was broadly tested achieving a testing accuracy of 92% to 94%

A siren identification system using deep learning to aid hearing-impaired people

The research presented in this paper is aiming to address the safety issue that hearing-impaired people are facing when it comes to identifying a siren sound. For that purpose, a siren identification system, using deep learning, was designed, built, and tested. The system consists of a convolutional neural network that used image recognition techniques to identify the presence of a siren by converting the incoming sound into spectrograms. The problem with the lack of datasets for the training of the network was addressed by generating the appropriate data using a variety of siren sounds mixed with relevant environmental noise. A hardware interface was also developed to communicate the detection of a siren with the user, using visual methods. After training the model, the system was extensively tested using realistic scenarios to assess its performance. For the siren sounds that were used for training, the system achieved an accuracy of 98 per cent. For real-world siren sounds, recorded in the central streets of London, the system achieved an accuracy of 91 per cent. When it comes to the operation of the system in noisy environments, the tests showed that the system can identify the presence of siren when this is at a sound level of up to -6 db below the background noise. These results prove that the proposed system can be used as a base for the design of a siren-identification application for hearing-impaired people

Automatic Heart Rate Detection during Sleep Using Tracheal Audio Recordings from Wireless Acoustic Sensor

Background: Heart rate is an essential diagnostic parameter indicating a patient’s condition. The assessment of heart rate is also a crucial parameter in the diagnostics of various sleep disorders, including sleep apnoea, as well as sleep/wake pattern analysis. It is usually measured using an electrocardiograph (ECG)—a device monitoring the electrical activity of the heart using several electrodes attached to a patient’s upper body—or photoplethysmography (PPG). Methods: The following paper investigates an alternative method for heart rate detection and monitoring that operates on tracheal audio recordings. Datasets for this research were obtained from six participants along with ECG Holter (for validation), as well as from fifty participants undergoing a full night polysomnography testing, during which both heart rate measurements and audio recordings were acquired. Results: The presented method implements a digital filtering and peak detection algorithm applied to audio recordings obtained with a wireless sensor using a contact microphone attached in the suprasternal notch. The system was validated using ECG Holter data, achieving over 92% accuracy. Furthermore, the proposed algorithm was evaluated against whole-night polysomnography-derived HR using Bland-Altman’s plots and Pearson’s Correlation Coefficient, reaching the average of 0.82 (0.93 maximum) with 0 BPM error tolerance and 0.89 (0.97 maximum) at ±3 BPM. Conclusions: The results prove that the proposed system serves the purpose of a precise heart rate monitoring tool that can conveniently assess HR during sleep as a part of a home-based sleep disorder diagnostics process