# DSP Published articles for DSP. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Sound Open Firmware 2.15 Released With AMD ACP 7.x Support, Intel UAOL DevFeed: [Sound Open Firmware 2.15 Released With AMD ACP 7.x Support, Intel UAOL]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-15T13:04:40Z Content type: release Language: en Sources: [Phoronix]() Topics: [Embedded Software Dev](), [Open Source](), [intel](), [Security](), [qemu](), [Xtensa](), [Fuzzing/Fuzz testing](), [POSIX](), [Testing]() Tags: [desktop-linux](), [driver](), [dsp](), [firmware](), [fuzzing](), [intel](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [open](), [open-source-graphics](), [phoronix](), [phoronix-test-suite](), [posix](), [processor](), [qemu](), [security](), [testing](), [ubuntu-benchmarks](), [ubuntu-hardware](), [usb](), [xtensa]() ### AI overview Sound Open Firmware 2.15 adds user-space, memory-protected module execution, AMD ACP 7.x and NXP i.MX8MP support, Intel UAOL USB audio offload, testing targets, security hardening, and new audio processing modules. ### Source excerpt Sound Open Firmware as the open-source, vendor-independent audio DSP firmware stack and driver framework is out today with a new feature update... ## Qualcomm and Amazon Sign Multi-Generation Deal for Custom AI Inference Silicon and 1.6T Optical Interconnects DevFeed: [Qualcomm and Amazon Sign Multi-Generation Deal for Custom AI Inference Silicon and 1.6T Optical Interconnects]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-08T17:17:46Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Inference](), [Inference Performance](), [AI Infrastructure](), [Amazon Web Services](), [data centers](), [Chip design](), [networking](), [Amazon Bedrock](), [amazon]() Tags: [ai](), [ai-data-centers](), [ai-inference](), [ai-infrastructure](), [amazon-bedrock](), [aws](), [chip-design](), [cost](), [data-centers](), [dsp](), [efficiency](), [enterprise](), [networking](), [partnership](), [qualcomm]() ### AI overview Qualcomm Technologies and Amazon are collaborating across multiple generations to develop custom silicon for AWS AI data centers, primarily targeting AI inference. The agreement also covers 1.6T optical connectivity for data center networks and Qualcomm's use of AWS infrastructure, including Amazon Bedrock, for electronic design automation workloads. ### Source excerpt Qualcomm Technologies and Amazon have entered into a multi-generation collaboration to deliver customized silicon at scale for AWS's AI data centers, with AI inference as the primary target. The agreement pairs Qualcomm's power-efficient processing, silicon design, and system-level integration with Amazon's AI infrastructure, and is aimed at the compute, memory bandwidth, networking, and energy constraints The post Qualcomm and Amazon Sign Multi-Generation Deal for Custom AI Inference Silicon and 1.6T Optical Interconnects appeared first on StorageReview.com. ## Qualcomm introduces Dragonwing Q-2390 and IQ-2390 for consumer and industrial AIoT applications DevFeed: [Qualcomm introduces Dragonwing Q-2390 and IQ-2390 for consumer and industrial AIoT applications]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-09-01T14:24:27Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Embedded Systems](), [cpu](), [Arm](), [RISC-V](), [GPU]() Tags: [4g-lte](), [android](), [artificial-intelligence-ai](), [camera](), [cortex-a55](), [cortex-a78](), [dsp](), [embedded](), [embedded-systems](), [hardware](), [hexagon](), [industrial](), [iot](), [linux](), [npu](), [processor](), [products](), [qualcomm](), [qualcomm-dragonwing](), [risc-v](), [ubuntu](), [wifi-5](), [yocto](), [zephyr](), [zephyr-os]() ### AI overview Qualcomm announced the Dragonwing Q-2390 and IQ-2390 quad-core Cortex-A78/A55 processors for consumer, commercial, and industrial AIoT applications. The Q-2390 targets devices such as retail and smart home products, while the IQ-2390 targets industrial automation, machine vision, building management, and energy systems. The processors include optional RISC-V real-time cores, Adreno graphics, Hexagon processing, camera and display interfaces, networking, and support for Android, Yocto Linux, Ubuntu, and Zephyr RTOS. ### Source excerpt Qualcomm has announced the Qualcomm Dragonwing Q-2390 and IQ-2390 quad-core Cortex-A78/A55 processors for AIoT consumer, commercial and industrial applications. The Dragonwing Q-2390 processor is designed for commercial and consumer devices, notably for applications such as retail and smart home products, as well as smart displays and enterprise edge devices, while the Dragonwing IQ-2390 processor is the SKU from the Dragonwing IQ2 Series designed for industrial automation, machine vision, building management and energy systems. Qualcomm IQ-2390 and Q-2390 specifications: CPU 1x Arm Cortex-A78 core up to 1.5 or 1.9 GHz depending on SKU 3x Arm Cortex-A55 cores up to 1.5 or 1.9 GHz depending on SKU Optional SiFive E61 RISC-V real-time core up to 600MHz with 768kB memory Cache - 512 KB L3 cache GPU - Qualcomm Adreno 704 up to 1.1 GHz Audio DSP - Qualcomm Hexagon V66 up to 1.1 GHz NPU - Qualcomm Hexagon up to 1.1 TOPS DPU [...] The post Qualcomm introduces Dragonwing Q-2390 and IQ-2390 for consumer and industrial AIoT applications appeared first on CNX Software - Embedded Systems News. ## PIE Instruction Extensions on ESP32-P4 and ESP32-S3 DevFeed: [PIE Instruction Extensions on ESP32-P4 and ESP32-S3]() Original publisher: [Read original article]() Author: John Lee Published: 2024-12-05T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [ESP32-P4](), [ESP32-S3](), [Assembly](), [Hardware](), [Inference](), [Artificial Intelligence](), [real-time]() Tags: [ai](), [algorithms](), [assembly](), [blog](), [dsp](), [esp32-p4](), [esp32-s3](), [hardware](), [inference](), [pie](), [real-time]() ### AI overview This tutorial examines PIE processor instruction extensions on ESP32-S3 and ESP32-P4. It covers their instruction architecture, formats, applications, and differences in hardware-loop support and accumulator size. ### Source excerpt The recent breakthroughs in artificial intelligence technology in such fields as image recognition, speech recognition, and natural language processing have opened up more possibilities for embedded system applications. When attempting to deploy AI model inference on embedded devices such as the ESP32-P4, we always strive to minimize inference time as much as possible to meet real-time requirements. ## Alexa DevFeed: [Alexa]() Original publisher: [Read original article]() Author: John Lee Published: 2018-10-24T00:00:00Z Content type: article Language: en Sources: [Blog on Developer Portal]() Topics: [SDKs](), [Espressif](), [Bluetooth](), [Internet of things](), [SOC](), [Amazon Web Services]() Tags: [alexa](), [apps](), [aws-iot](), [blog](), [bluetooth](), [developers](), [devices](), [dsp](), [embedded](), [esp32](), [espressif](), [iot](), [local](), [music](), [sdk](), [soc](), [technology](), [voice]() ### AI overview This archival article describes Espressif's Alexa SDKs for building Alexa-enabled devices with embedded microphones and speakers. It covers Alexa Voice Services support for speakers and an AVS integration for smart devices using AWS IoT, including voice and device-data exchange. The project is no longer actively maintained. ### Source excerpt This project is no longer actively maintained. The information in this article is provided for archival and reference purposes only, and developers are encouraged to explore Espressif's latest solutions for voice-enabled applications. Espressif's Alexa SDK makes building Alexa-powered devices a breeze. These devices have microphones and speaker embedded within the device to provide the full Alexa experience. ## Anatomy of a Voice-Integrated Device DevFeed: [Anatomy of a Voice-Integrated Device]() Original publisher: [Read original article]() Author: John Lee Published: 2018-08-12T00:00:00Z Content type: article Language: en Sources: [Blog on Developer Portal]() Topics: [Hardware](), [ESP32](), [Algorithms, Complexity](), [Wi-Fi]() Tags: [alexa](), [audio](), [blog](), [connectivity](), [dsp](), [embedded](), [esp32](), [framework](), [hardware](), [interface](), [speakers](), [voice](), [voice-assistant](), [wi-fi]() ### AI overview This article explains the internal components and audio-processing path of a voice-integrated device using Alexa as an example. It describes local wake-word detection, cloud processing of the remaining command, and hardware options including ESP32, connectivity processors, digital signal processors, microphones, and speakers. ### Source excerpt Recently I have seen significant interest from OEMs in embedding voice assistants, like Alexa, directly into their products. Not just linking with a voice-assistant using a cloud to cloud integration, but by having the product itself be voice-enabled (with microphones and speakers). For example, a washing machine with a voice interface for starting it, or a smart switch with "Echo" embedded into it. Let us have a quick look at the internal components of a typical Voice-Integrated device. ## CTCSS fingerprinting: a method for transmitter identification DevFeed: [CTCSS fingerprinting: a method for transmitter identification]() Original publisher: [Read original article]() Author: Oona Räisänen (noreply@blogger.com) Published: 2016-10-07T13:17:00Z Content type: article Language: en Sources: [Oona Räisänen]() Topics: [Programming](), [Streams](), [Library](), [USB]() Tags: [algorithms](), [analysis](), [audio](), [communications](), [dsp](), [library](), [quality](), [radio](), [security](), [side-channels](), [signal](), [usb](), [voice](), [walkie-talkie]() ### AI overview The article investigates whether the 88.5 Hz CTCSS tone in hand-held FM transceivers can fingerprint transmitters by revealing small differences in crystal frequencies and modulation depths. It describes analyzing walkie-talkie recordings with liquid-dsp and a phase-locked loop. ### Source excerpt Identifying unknown radio transmitters by their signals is called radio fingerprinting. It is usually based on rise-time signatures, i.e. characteristic differences in how the transmitter frequency fluctuates at carrier power-up. Here, instead, I investigate the fingerprintability of another feature in hand-held FM transceivers, known as CTCSS or Continuous Tone-Coded Squelch System. Motivation & data I came across a long, losslessly compressed recording of some walkie-talkie chatter and wanted to know more about it, things like the number of participants and who's talking with who. I started writing a transcript - a fun pastime - but some voices sounded so similar I wondered if there was a way to tell them apart automatically. The file comprises several thousand short transmissions as FM demodulated audio lowpass filtered at 4500 Hz. Signal quality is variable; most transmissions are crisp and clear but some are buried under noise. Passages with no signal are squelched to zero. I considered several potentially fingerprintable features, many of them unrealistic: Carrier power-up; but many transmissions were missing the very beginning because of squelch Voice identification; but it would probably require pretty sophisticated algorithms (too difficult!) and longer samples Mean audio power; but it's not consistent enough, as it depends on text, tone of voice, etc. Maximum audio power; but it's too sensitive to peaks in FM noise I then noticed all transmissions had a very low tone at 88.5 Hz. It turned out to be CTCSS, an inaudible signal that enables handsets to silence unwanted transmissions on the same channel. This gave me an idea inspired by mains frequency analysis: Could this tone be measured to reveal minute differences in crystal frequencies and modulation depths? Also, knowing that these were recorded using a cheap DVB-T USB stick - would it have a stable enough oscillator to produce consistent measurements? Measurements I used the liquid-dsp library for signa ## Receiving RDS with the RTL-SDR DevFeed: [Receiving RDS with the RTL-SDR]() Original publisher: [Read original article]() Author: Oona Räisänen (noreply@blogger.com) Published: 2015-02-08T19:10:00Z Content type: tutorial Language: en Sources: [Oona Räisänen]() Topics: [Command-line interface](), [Tool](), [C](), [Code](), [Linux]() Tags: [c](), [code](), [command-line](), [development](), [dsp](), [fm-radio](), [linux](), [perl](), [receiver](), [signal](), [software](), [tool]() ### AI overview This article explains redsea, a command-line RDS decoder that supports RTL-SDR receivers through rtl_fm. It describes the tool's dependencies and operation, including the C99 DSP receiver, signal synchronization, symbol decoding, and subsequent RDS group processing by redsea.pl. ### Source excerpt redsea is a command-line RDS decoder. I originally wrote it as a script to decode RDS from demultiplexed FM stereo sound. Later I've experimented with other ways to read the bits, and the latest addition is to support the RTL-SDR television receiver via the rtl_fm tool. Redsea is on GitHub. It has minimal dependencies (perl core modules, C standard library, rtl-sdr command-line tools) and has been tested to work on OSX and Linux with good enough FM reception. All test results, ideas, and pull requests are welcome. Update 12/2016: Redsea has seen a lot of development since this post was written; see Redsea 0.7, a lightweight RDS decoder. What it says The program prints out decoded RDS groups, one group per line. Each group will contain a PI code identifying the station plus varying other data, depending on the group type. The below picture explains the types of data you'll probably most often encounter. A more verbose output can be enabled with the -l option (it contains the same information though). The -t option prefixes all groups with an ISO timestamp. How it works The DSP side of my program, named rtl_redsea, is written in C99. It's a synchronous DBPSK receiver that first bandpass filters ① the multiplex signal. A PLL locks onto the 19 kHz stereo pilot tone; its third harmonic (57 kHz) is used to regenerate the RDS subcarrier. Dividing it by 16 also gives us the 1187.5 Hz clock frequency. Phase offsets of these derived signals are adjusted separately. The local 57 kHz carrier is synchronized so that the constellation lines up on the real axis, so we can work on the real part only ②. Biphase symbols are multiplied by the square-wave clock and integrated ③ over a clock period, and then dumped into a delta decoder ④, which outputs the binary data as bit strings into stdout ⑤. Signal quality is estimated a couple of times per second by counting the number of "suspicious" integrated biphase symbols, i.e. symbols with halves of opposite signs. The symbols are being sa