Regression and Classification for Direction-of-Arrival Estimation with Convolutional Recurrent Neural Networks

We present a novel learning-based approach to estimate the direction-of-arrival (DOA) of a sound source using a convolutional recurrent neural network (CRNN) trained via regression on synthetic data and Cartesian labels. We also describe an improved method to generate synthetic data to train the neural network using state-of-the-art sound propagation algorithms that model specular as well as diffuse reflections of sound. We compare our model against three other CRNNs trained using different formulations of the same problem: classification on categorical labels, and regression on spherical coordinate labels. In practice, our model achieves up to 43% decrease in angular error over prior methods. The use of diffuse reflection results in 34% and 41% reduction in angular prediction errors on LOCATA and SOFA datasets, respectively, over prior methods based on image-source methods. Our method results in an additional 3% error reduction over prior schemes that use classification based networks, and we use 36% fewer network parameters

Implementation of an Autonomous Impulse Response Measurement System

Data collection is crucial for researchers, as it can provide important insights for describing phenomena. In acoustics, acoustic phenomena are characterized by Room Impulse Responses (RIRs) occurring when sound propagates in a room. Room impulse responses are needed in vast quantities for various reasons, including the prediction of acoustical parameters and the rendering of virtual acoustical spaces. Recently, mobile robots navigating within indoor spaces have become increasingly used to acquire information about its environment. However, little research has attempted to utilize robots for the collection of room acoustic data. This thesis presents an adaptable automated system to measure room impulse responses in multi-room environments, using mobile and stationary measurement platforms. The system, known as Autonomous Impulse Response Measurement System (AIRMS), is divided into two stages: data collection and post-processing. To automate data collection, a mobile robotic platform was developed to perform acoustic measurements within a room. The robot was equipped with spatial microphones, multiple loudspeakers and an indoor localization system, which reported real time location of the robot. Additionally, stationary platforms were installed in specific locations inside and outside the room. The mobile and stationary platforms wirelessly communicated with one another to perform the acoustical tests systematically. Since a major requirement of the system is adaptability, researchers can define the elements of the system according to their needs, including the mounted equipment and the number of platforms. Post-processing included extraction of sine sweeps and the calculation of impulse responses. Extraction of the sine sweeps refers to the process of framing every acoustical test signal from the raw recordings. These signals are then processed to calculate the room impulse responses. The automatically collected information was complemented with manually produced data, which included rendering of a 3D model of the room, a panoramic picture. The performance of the system was tested under two conditions: a single-room and a multiroom setting. Room impulse responses were calculated for each of the test conditions, representing typical characteristics of the signals and showing the effects of proximity from sources and receivers, as well as the presence of boundaries. This prototype produces RIR measurements in a fast and reliable manner. Although some shortcomings were noted in the compact loudspeakers used to produce the sine sweeps and the accuracy of the indoor localization system, the proposed autonomous measurement system yielded reasonable results. Future work could expand the amount of impulse response measurements in order to further refine the artificial intelligence algorithms

Establishment of a Beamforming Dataset on Basic Models of Low-Speed Axial Fan Blade Sections

The paper presents wind tunnel experiments, supplemented with phased array microphone measurements, on 2D basic models of low-speed axial fan blade sections: a flat plate, a cambered plate, and a RAF6-E airfoil. It aims at documenting the establishment of an acoustic beamforming dataset for the three profiles. The phased array microphone measurements offer spatially resolved information on the generated noise. The measurement setup enables the correlation of the streamwise evolution of the blade boundary layer with the associated noise characteristics. The dataset incorporates a wide range of incidence and Reynolds-numbers investigated. The present paper is confined to reporting on experimental results for arbitrarily selected representative incidences, Reynolds numbers, frequency bands, and profiles. The paper outlines a methodology for the evaluation and representation of the beamforming data in the following forms: source strength level based third-octave spectra obtained using background noise subtraction; maps presenting the loci of source strength level maxima; noise source maps for frequency bands of anticipated vortex shedding noise

Investigation of Anti-Phase Asymmetric Quiet Rotor Technology

The future of urban air mobility has a well-known tall pole challenge in the form of community acceptance which largely comes from the noise. This paper presents a proposed anti-phase rotor technology that could reduce noise sources such as blade vortex interaction noise. The anti-phase rotor technology includes a rotor design with various anti-phase alternating trailing edge patterns and a rotor design with an asymmetric blade tip. Four small-scale anti-phase rotors are fabricated by 3D printing for acoustic measurements conducted in a low-speed open-circuit wind tunnel to assess the effectiveness of the proposed anti-phase rotor technology. Preliminary test results appear to be promising and indicate that the anti-phase rotor designs could be a practical means of reducing blade vortex interactions and noise. The four tested anti-phase rotor designs have peak acoustic performance depending on the RPM and thrust which suggests improved performance through design optimization could be achieved for specific mission requirements

Development of an automated speech recognition interface for personal emergency response systems

<p>Abstract</p> <p>Background</p> <p>Demands on long-term-care facilities are predicted to increase at an unprecedented rate as the baby boomer generation reaches retirement age. Aging-in-place (i.e. aging at home) is the desire of most seniors and is also a good option to reduce the burden on an over-stretched long-term-care system. Personal Emergency Response Systems (PERSs) help enable older adults to age-in-place by providing them with immediate access to emergency assistance. Traditionally they operate with push-button activators that connect the occupant via speaker-phone to a live emergency call-centre operator. If occupants do not wear the push button or cannot access the button, then the system is useless in the event of a fall or emergency. Additionally, a false alarm or failure to check-in at a regular interval will trigger a connection to a live operator, which can be unwanted and intrusive to the occupant. This paper describes the development and testing of an automated, hands-free, dialogue-based PERS prototype.</p> <p>Methods</p> <p>The prototype system was built using a ceiling mounted microphone array, an open-source automatic speech recognition engine, and a 'yes' and 'no' response dialog modelled after an existing call-centre protocol. Testing compared a single microphone versus a microphone array with nine adults in both noisy and quiet conditions. Dialogue testing was completed with four adults.</p> <p>Results and discussion</p> <p>The microphone array demonstrated improvement over the single microphone. In all cases, dialog testing resulted in the system reaching the correct decision about the kind of assistance the user was requesting. Further testing is required with elderly voices and under different noise conditions to ensure the appropriateness of the technology. Future developments include integration of the system with an emergency detection method as well as communication enhancement using features such as barge-in capability.</p> <p>Conclusion</p> <p>The use of an automated dialog-based PERS has the potential to provide users with more autonomy in decisions regarding their own health and more privacy in their own home.</p

Locational wireless and social media-based surveillance

The number of smartphones and tablets as well as the volume of traffic generated by these devices has been growing constantly over the past decade and this growth is predicted to continue at an increasing rate over the next five years. Numerous native features built into contemporary smart devices enable highly accurate digital fingerprinting techniques. Furthermore, software developers have been taking advantage of locational capabilities of these devices by building applications and social media services that enable convenient sharing of information tied to geographical locations. Mass online sharing resulted in a large volume of locational and personal data being publicly available for extraction. A number of researchers have used this opportunity to design and build tools for a variety of uses – both respectable and nefarious. Furthermore, due to the peculiarities of the IEEE 802.11 specification, wireless-enabled smart devices disclose a number of attributes, which can be observed via passive monitoring. These attributes coupled with the information that can be extracted using social media APIs present an opportunity for research into locational surveillance, device fingerprinting and device user identification techniques. This paper presents an in-progress research study and details the findings to date

SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning

We introduce SoundSpaces 2.0, a platform for on-the-fly geometry-based audio rendering for 3D environments. Given a 3D mesh of a real-world environment, SoundSpaces can generate highly realistic acoustics for arbitrary sounds captured from arbitrary microphone locations. Together with existing 3D visual assets, it supports an array of audio-visual research tasks, such as audio-visual navigation, mapping, source localization and separation, and acoustic matching. Compared to existing resources, SoundSpaces 2.0 has the advantages of allowing continuous spatial sampling, generalization to novel environments, and configurable microphone and material properties. To our knowledge, this is the first geometry-based acoustic simulation that offers high fidelity and realism while also being fast enough to use for embodied learning. We showcase the simulator's properties and benchmark its performance against real-world audio measurements. In addition, we demonstrate two downstream tasks -- embodied navigation and far-field automatic speech recognition -- and highlight sim2real performance for the latter. SoundSpaces 2.0 is publicly available to facilitate wider research for perceptual systems that can both see and hear.Comment: Camera-ready version. Website: https://soundspaces.org. Project page: https://vision.cs.utexas.edu/projects/soundspaces