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A sound card (also known as an audio card) is an internal expansion card that provides input and output of audio signals to and from a computer under the control of computer programs. The term sound card is also applied to external audio interfaces used for professional audio applications. Sound functionality can also be integrated onto the motherboard, using components similar to those found on plug-in cards. The integrated sound system is often still referred to as a sound card. Sound processing hardware is also present on modern video cards with HDMI to output sound along with the video using that connector; previously they used a S/PDIF connection to the motherboard or sound card. Typical uses of sound cards or sound card functionality include providing the audio component for multimedia applications such as music composition, editing video or audio, presentation, education and entertainment (games) and video projection. Sound cards are also used for computer-based communicatio

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Mathias Josef Payer, Hui Peng

The Universal Serial Bus (USB) connects external devices to a host. This interface exposes the OS kernels and device drivers to attacks by malicious devices. Unfortunately, kernels and drivers were developed under a security model that implicitly trusts connected devices. Drivers expect faulty hardware but not malicious attacks. Similarly, security testing drivers is challenging as input must cross the hardware/software barrier. Fuzzing, the most widely used bug finding technique, relies on providing random data to programs. However, fuzzing device drivers is challenging due to the difficulty in crossing the hardware/software barrier and providing random device data to the driver under test. We present USBFuzz, a portable, flexible, and modular framework for fuzz testing USB drivers. At its core, USBFuzz uses a software-emulated USB device to provide random device data to drivers (when they perform IO operations). As the emulated USB device works at the device level, porting it to other platforms is straight-forward. Using the USBFuzz framework, we apply (i) coverage-guided fuzzing to a broad range of USB drivers in the Linux kernel; (ii) dumb fuzzing in FreeBSD, MacOS, and Windows through cross-pollination seeded by the Linux inputs; and (iii) focused fuzzing of a USB webcam driver. USBFuzz discovered a total of 26 new bugs, including 16 memory bugs of high security impact in various Linux subsystems (USB core, USB sound, and network), one bug in FreeBSD, three in MacOS (two resulting in an unplanned reboot and one freezing the system), and four in Windows 8 and Windows 10 (resulting in Blue Screens of Death), and one bug in the Linux USB host controller driver and another one in a USB camera driver. From the Linux bugs, we have fixed and upstreamed 11 bugs and received 10 CVEs.

USENIX ASSOC2020

On-the-Fly Audio Source Separation-A Novel User-Friendly Framework

Dalia Salem Hassan Fahmy El Badawy

This paper addresses the challenging problem of single-channel audio source separation. We introduce a novel userguided framework where source models that govern the separation process are learned on-the-fly from audio examples retrieved online. The user only provides the search keywords that describe the sources in the mixture. In this framework, the generic spectral characteristics of each source are modeled by a universal sound class model learned from the retrieved examples via nonnegative matrix factorization. We propose several group sparsity-inducing constraints in order to efficiently exploit a relevant subset of the universal model adapted to the mixture to be separated. We then derive the corresponding multiplicative update rules for parameter estimation. Separation results obtained from automated and user tests on mixtures containing various types of sounds confirm the effectiveness of the proposed framework.

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Shapes From Echoes: Uniqueness From Point-to-Plane Distance Matrices

Ivan Dokmanic, Miranda Krekovic, Martin Vetterli

We study the problem of localizing a configuration of points and planes from the collection of point-to-plane distances. This problem models simultaneous localization and mapping from acoustic echoes as well as the & x201C;structure from sound & x201D; approach to microphone localization with unknown sources. In our earlier work we proposed computational methods for localization from point-to-plane distances and noted that such localization suffers from various ambiguities beyond the usual rigid body motions; in this paper we provide a complete characterization of uniqueness. We enumerate all cases of configurations which lead to the same distance measurements as a function of the number of planes and points, and algebraically characterize the related transformations in both 2D and 3D.

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