10 research outputs found
Pyroomacoustics: A Python package for audio room simulations and array processing algorithms
We present pyroomacoustics, a software package aimed at the rapid development
and testing of audio array processing algorithms. The content of the package
can be divided into three main components: an intuitive Python object-oriented
interface to quickly construct different simulation scenarios involving
multiple sound sources and microphones in 2D and 3D rooms; a fast C
implementation of the image source model for general polyhedral rooms to
efficiently generate room impulse responses and simulate the propagation
between sources and receivers; and finally, reference implementations of
popular algorithms for beamforming, direction finding, and adaptive filtering.
Together, they form a package with the potential to speed up the time to market
of new algorithms by significantly reducing the implementation overhead in the
performance evaluation step.Comment: 5 pages, 5 figures, describes a software packag
FRIDA: FRI-Based DOA Estimation for Arbitrary Array Layouts
In this paper we present FRIDA---an algorithm for estimating directions of
arrival of multiple wideband sound sources. FRIDA combines multi-band
information coherently and achieves state-of-the-art resolution at extremely
low signal-to-noise ratios. It works for arbitrary array layouts, but unlike
the various steered response power and subspace methods, it does not require a
grid search. FRIDA leverages recent advances in sampling signals with a finite
rate of innovation. It is based on the insight that for any array layout, the
entries of the spatial covariance matrix can be linearly transformed into a
uniformly sampled sum of sinusoids.Comment: Submitted to ICASSP201
Sparse Recovery of Strong Reflectors With an Application to Non-Destructive Evaluation
In this paper we show that it is sufficient to recover the locations of K strong reflectors within an insonified medium from three receive elements and 2K+1 samples per element. The proposed approach leverages advances in sampling signals with a finite rate of innovation along each element and rank properties from the Euclidean distance matrix construction across elements. With the proposed approach, it is not necessary to construct an image in order to identify strong reflective sources, which is why much fewer receive elements are needed. However, the assumed transmit scheme still uses a standard linear array in order to excite the entire medium with sufficient energy. The approach is validated with simulated data and a measurement that emulates a scenario in non-destructive evaluation
Morphological component analysis for sparse regularization in plane wave imaging
Classical ultrasound image reconstruction mainly relies on the well-known delay-and-sum (DAS) beamforming for its simplicity and real-time capability. Sparse regularization methods propose an alternative to DAS which lead to a better inversion of the ill-posed problem resulting from the acoustic wave propagation. In the following work, a new sparse regularization method is proposed which includes a component-based modelling of the radio-frequency images as well as a pointspread- function-adaptive sparsity prior. The proposed method, evaluated on the PICMUS dataset,outperforms the classical DAS in terms of contrast and resolution
Hardware And Software For Reproducible Research In Audio Array Signal Processing
In our demo, we present two hardware platforms for prototyping audio array signal processing. Pyramic is a 48-channel microphone array fitted on an FPGA and Compact Six is a portable microphone array with six microphones, closer to the technical constraints of consumer electronics. A browser based interface was developed that allows the user to interact with the audio stream from the arrays in real time. The software component of this demo is a Python module with implementations of basic audio signal processing blocks and popular techniques like STFT, beamforming, and DoA. Both the hardware design files and the software are open source and freely shared. As part of a collaboration with IBM Research, their beamforming and imaging technologies will also be portrayed. The hardware will be demonstrated through an installation processing the microphone signals into light patterns on a circular LED array. The demo will be interactive and let visitors play with different algorithms for DoA (SRP, FRIDA [1], Bluebild) and beamforming (MVDR, Flexibeam [2]). The availability of an open platform with reference implementations encourages reproducible research and minimizes setup-time when testing and benchmarking new audio array signal processing algorithms. It can also serve as a useful educational tool, providing a means to work with real-life signals
Learning rich optical embeddings for privacy-preserving lensless image classification
By replacing the lens with a thin optical element, lensless imaging enables
new applications and solutions beyond those supported by traditional camera
design and post-processing, e.g. compact and lightweight form factors and
visual privacy. The latter arises from the highly multiplexed measurements of
lensless cameras, which require knowledge of the imaging system to recover a
recognizable image. In this work, we exploit this unique multiplexing property:
casting the optics as an encoder that produces learned embeddings directly at
the camera sensor. We do so in the context of image classification, where we
jointly optimize the encoder's parameters and those of an image classifier in
an end-to-end fashion. Our experiments show that jointly learning the lensless
optical encoder and the digital processing allows for lower resolution
embeddings at the sensor, and hence better privacy as it is much harder to
recover meaningful images from these measurements. Additional experiments show
that such an optimization allows for lensless measurements that are more robust
to typical real-world image transformations. While this work focuses on
classification, the proposed programmable lensless camera and end-to-end
optimization can be applied to other computational imaging tasks.Comment: 29 pages, 23 figures, under revie
LenslessPiCam: A Hardware and Software Platform for Lensless Computational Imaging with a Raspberry Pi
Lensless imaging seeks to replace/remove the lens in a conventional imaging
system. The earliest cameras were in fact lensless, relying on long exposure
times to form images on the other end of a small aperture in a darkened
room/container (camera obscura). The introduction of a lens allowed for more
light throughput and therefore shorter exposure times, while retaining sharp
focus. The incorporation of digital sensors readily enabled the use of
computational imaging techniques to post-process and enhance raw images (e.g.
via deblurring, inpainting, denoising, sharpening). Recently, imaging
scientists have started leveraging computational imaging as an integral part of
lensless imaging systems, allowing them to form viewable images from the highly
multiplexed raw measurements of lensless cameras (see [5] and references
therein for a comprehensive treatment of lensless imaging). This represents a
real paradigm shift in camera system design as there is more flexibility to
cater the hardware to the application at hand (e.g. lightweight or flat
designs). This increased flexibility comes however at the price of a more
demanding post-processing of the raw digital recordings and a tighter
integration of sensing and computation, often difficult to achieve in practice
due to inefficient interactions between the various communities of scientists
involved. With LenslessPiCam, we provide an easily accessible hardware and
software framework to enable researchers, hobbyists, and students to implement
and explore practical and computational aspects of lensless imaging. We also
provide detailed guides and exercises so that LenslessPiCam can be used as an
educational resource, and point to results from our graduate-level signal
processing course
A Study on More Realistic Room Simulation for Far-Field Keyword Spotting
We investigate the impact of more realistic room simulation for training far-field keyword spotting systems without fine-tuning on in-domain data. To this end, we study the impact of incorporating the following factors in the room impulse response (RIR) generation: air absorption, surface- and frequency-dependent coefficients of real materials, and stochastic ray tracing. Through an ablation study, a wake word task is used to measure the impact of these factors in comparison with a ground-truth set of measured RIRs. On a hold-out set of re-recordings under clean and noisy far-field conditions, we demonstrate up to 35.8% relative improvement over the commonly-used (single absorption coefficient) image source method. Source code is made available in the Pyroomacoustics package, allowing others to incorporate these techniques in their work