audio News - Page 14 of 26 - CNX Software

March 9, 2021March 9, 2021 by Jean-Luc Aufranc (CNXSoft) - 3 Comments

Bluetrum AB32VG1 board features AB5301A Bluetooth RISC-V MCU, runs RT-Thread RTOS

Bluetrum, a Shenzhen-based fabless company focusing on audio chips, has designed the AB32VG1 board based on its AB5301A Bluetooth RISC-V MCU for general-purpose and audio applications. The board comes with a MicroSD card, a USB port, a 3.5mm audio jack, and Arduino UNO compatible headers for easy prototyping. Bluetrum AB32VG1 board preliminary specifications: MCU – Bluetrum AB5301A RISC-V MCU @ 120 MHz (overclockable to 192 MHz) with 192KB RAM, 1MB flash, Bluetooth 5.0 connectivity and FM radio; LQFP48 package Storage – MicroSD card slot Audio – 3.5mm audio jack, 3-pin microphone header Connectivity – Bluetooth 5.0 with PCB antenna USB – USB 2.0 host port, USB Type-C port for power and programming Expansion – Arduino UNO header with 1x I2C, 6x ADC, 6x PWM Misc – IR receiver, 3x user buttons, 1x reset button, RGB LED, power LED, status LEDs, various configuration jumper Power Supply – 5V via USB Type-C […]

March 7, 2021March 6, 2021 by Saumitra Jagdale - No Comments

QuickLogic’s Hearable Reference Design enables Alexa Voice-Initiated Devices

Working with QuickLogic's Smart Hearable Reference Design

Last year we saw QuickFeather board featuring EOS S3 Cortex-M4F MCU with embedded FPGA, which was a crowdfunding project. This year the company has launched its smart hearable reference design based on a similar processor by QuickLogic. The device is dedicated to “Voice-Initiated, Hands-Free, Alexa Built-In Devices with Close-Talk Support.” QuickLogic’s smart hearable reference design is based on the company’s Open Reconfigurable Computing (QORC) which supports a complete open-source set of development options for the MCU and FPGA devices. It is also built on the EOS S3 Voice Processor and the QuickFeather open source development kit. This can enhance the user experience with a longer battery life of the device. The EOS S3 Arm Cortex- M4 processor features Low Power Sound Detection (LPSD) technology along with DSP Concepts’ TalkTo noise suppression and beamforming technology for the directional transmission of signals. It also comes with Alexa Wake Word engine technology, an […]

February 28, 2021February 28, 2021 by Jean-Luc Aufranc (CNXSoft) - 18 Comments

Lyra audio codec enables high-quality voice calls at 3 kbps bitrate

We’re often writing about new video codecs like AV1 or H.266, and recently, we covered AVIF picture format that offers an improved quality/compression ratio against WebP and JPEG, but there’s also work done on audio codecs. Notably, we noted Opus 1.2 offered decent speech quality with a bitrate as low as 12 kbps when it was outed in 2017, the release of Opus 1.3 in 2019 improved the codec further with high-quality speech possible at just 9 kbps. But Google AI recently unveiled Lyra very low-bitrate codec for speech compression that achieves high speech quality with a bitrate as low as 3kbps. Before we go into the details of Lyra codec, Google compared a reference audio file encoded with Lyra at 3 kbps, Opus at 6 kbps (the minimum bitrate for Opus), and Speex at 3 kbps, and users reported Lyra to sound the best, and close to the original. […]

February 17, 2021February 18, 2021 by Jean-Luc Aufranc (CNXSoft) - 3 Comments

MaaXBoard Nano SBC targets audio and edge IoT with NXP i.MX 8M Nano SoC

Avnet has launched several Raspberry Pi-inspired MaaXBoard SBCs based on NXP i.MX processors through their Embest subsidiary starting in 2019 with MaaXBoard single board computer powered by an NXP i.MX 8M processor, and following by MaaxBoard Mini with NXP i.NX 8M Mini SoC in 2020. The latest model is MaaXBoard Nano SBC with an NXP i.MX 8M Nano quad-core Cortex-A53 processor best suited to audio and edge IoT applications. MaaxBoard Nano SBC specifications: SoC – NXP i.MX 8M Nano quad-core Arm Arm Cortex-A53 processor @ up to 1.5GHz with Cortex-M7F core @ 750MHz, 2D GPU, 3D GPU, but no video hardware decoding. System Memory -1GB DDR4 SDRAM Storage – 16GB eMMC flash, 256 Mbit QSPI Flash, MicroSD Slot Display Interface – MIPI DSI display Interface Audio – 3.5mm audio jack, 4x built-in microphones Camera I/F – MIPI CSI Camera Interface Networking – Gigabit Ethernet, 802.11 b/g/n/ac WiFi 5, Bluetooth 4.2/5 […]

February 15, 2021February 16, 2021 by Jean-Luc Aufranc (CNXSoft) - 37 Comments

AmpliPi – A Raspberry Pi-based whole house audio amplifier (Crowdfunding)

AmpliPi Raspberry Pi Whole House Amplifier

Micro Nova has put together an open-source, whole-house audio amplifier called AmpliPi based on Raspberry Pi Compute Module 3+. It is capable of streaming four independent sources to 6 stereo output zones, expandable to up to 36 stereo output zones through daisy-chained extender units. AmpliPi specifically supports inputs from four networking streaming sources including AirPlay, Pandora, Spotify, and DLNA, as well as four analog RCA inputs for your media appliances. AmpliPi key components and features: Controller Board Carrier board fitted with Raspberry Pi Compute Module 3+ and PCM5102A & CM6206 audio DACs. It also communicates over I2C with the STM32 MCU on the Preamp board (see below) to control the muxing and amplification systems. Interfaces 10/100M Ethernet port HDMI 1.4 output 2x USB 2.0 ports, plus one internal USB port Service and console ports for maintenance and/or debugging. Preamp Board Board equipped with a 6×4 audio matrix switching system and […]

February 9, 2021February 9, 2021 by Jean-Luc Aufranc (CNXSoft) - 2 Comments

iPod Classic given new life with Raspberry Pi Zero W & Spotify

Guy Dupont got a bunch of 2004, fourth-generation iPod Classic MP3 players from his mother-in-law, and instead of playing MP3 files on the media players, he decided to repurpose one with a Raspberry Pi Zero W to be able to stream music from Spotify over WiFi. The resulting project is called sPot (ess-pot), and looks just like an original iPod, but it’s a Linux device that can stream/search via Spotify with a UI written in Python and based on the original iPod experience. But apart from the enclosure, and the original “click wheel” there’s not much left from the original design. Besides the Raspberry Pi Zero W SBC and iPod enclosure, the sPod includesAdafruit Mini LiPoly/LiIon USB Charger and PowerBoost 1000 Basic boards for charging and power management, a 1,000mah,3.7V rechargeable li-ion battery, vibration motor discs For haptic feedback, a 2-inch Adafruit TFT display, and a few other components, wires, […]

January 31, 2021 by Jean-Luc Aufranc (CNXSoft) - 12 Comments

K210 AI Accelerator Raspberry Pi pHAT targets secure AIoT projects (Crowdfunding)

Kendryte K210 is a dual-core RISC-V AI processor that was launched in 2018 and found in several smart audio and computer vision solutions. We previously wrote a Getting Started Guide for Grove AI HAT for Raspberry Pi using Arduino and MicroPython, and XaLogic XAPIZ3500 offered an even more compact K210 solution as a Raspberry pi pHAT with Raspberry Pi Zero form factor. The company is now back with another revision of the board called “XaLogic K210 AI accelerator” designed to work with Raspberry Pi Zero and larger boards with the 40-pin connector. K210 AI Accelerator board specifications: SoC – Kendryte K210 dual-core 64-bit RISC-V processor @ 400 MHz with 8MB on-chip RAM, various low-power AI accelerators delivering up to 0.5 TOPS, Host Interface – 40-pin Raspberry Pi header using: SPI @ 40 MHz via Lattice iCE40 FPGA I2C, UART, JTAG, GPIOs signals Security Infineon Trust-M cloud security chip 128-bit AES […]

January 11, 2021January 10, 2021 by Saumitra Jagdale - No Comments

DBM10 AI SoC is optimized for battery-powered voice and sensor processing

DSP Group announced DBM10 a low-power AI/ML-enabled dual-core SoC. The SoC is equipped with a DSP (Digital Signal Processor) and a dedicated nNetLite NN (Neural Network) processor that improves voice and sensor processing and ensures low-power consumption when working with sufficient-sized neural networks. Key Specifications of NN Processor Form factor: ~4 mm2 Ultra-low-power inference consumption – ~500 µW (typical) for voice NN algorithms Runs Hello Edge 30-word detection model @ 1 MHz (125 MHz available) Allows porting of large models (10s of megabytes) without significant accuracy loss using model optimization and compression. DBM10 AI SoC uses the combined functioning of machine learning, voice, and sensor parameters. This includes voice trigger (VT), voice authentication (VA), voice command (VC), noise reduction (NR), acoustic echo cancellation (AEC), sound event detection (SED), proximity and gesture detection, sensor data processing, and equalization. The DBM10 is suitable for battery-operated devices like smartphones, tablets, and wearables. It […]