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    FREE LIVE E-Workshop on TinyML for beginners by Salman Faris on 12th October 2023 at 6 PM.

    TinyML
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    • salmanfaris
      salmanfaris last edited by salmanfaris

      🚀 Join FREE Live E-workshop on TinyML for beginners! 🚀

      Are you passionate about the future of machine learning and its applications in the tiniest of devices? Then, this is a workshop you don't want to miss!

      📌 Event Details:
      📆 Date: 12th October 2023
      🕒 Time: 6 PM
      🌐 Location: Online (Microsoft Teams)
      🔗 Register now to secure your spot: https://lnkd.in/dYcZ3p-x

      Tiny Machine Learning (TinyML) is revolutionizing the world of IoT and embedded systems, enabling AI and ML on microcontrollers, sensors, and other edge devices. Our FREE live e-workshop is your gateway to understanding the principles, tools, and practical applications of TinyML.

      📈 Workshop Highlights:
      ✅ Introduction to TinyML and its significance
      ✅ LIVE Demo of Easy to make TinyML Projects
      ✅ Q&A Sessions to solve your doubts
      ✅ Networking opportunities with fellow enthusiasts

      Whether you're an experienced ML practitioner or just curious about this exciting field, our workshop is designed to cater to all levels of expertise. Don't miss this chance to unlock the potential of TinyML and gain valuable insights into the future of intelligent, edge-driven devices.

      🔗 Register now to secure your spot: Here

      Don't miss out on this opportunity to stay at the forefront of innovation. Join us for an interactive and informative session on TinyML. See you there!

      084eace3-125a-445b-8013-9f4880e7a64b-image.png

      salmanfaris 1 Reply Last reply Reply Quote 0
      • salmanfaris
        salmanfaris @salmanfaris last edited by

        You can purchase XIAO ESP32-S3 here at https://robu.in/product/seeed-studio-xiao-esp32s3-sense-2-4ghz-wi-fi-ble-5-0-ov2640-camera-sensor-digital-microphone-battery-charge-supported-rich-interface-iot-embedded-ml/

        SeeedStudio tinyML offline course details here at https://wiki.seeedstudio.com/tinyml_workshop_course_new/

        Please check and let us know if you have questions.

        1 Reply Last reply Reply Quote 0
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          I'm developing an audio classification project with four classes using an RNN with LSTM. While the model performs well in classifying audio in silent environments, it struggles with noisy situations like crowds or disturbances. How can I train the model more robustly to accurately predict classifications in such challenging environments?

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          @salmanfaris No, I haven't made any progess on the matter

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          I have been working on a project . In which I want to capture data using the PDM mic and trasmit it through the BLE available . I am new to hardware and these sensors so I am unable to make sense of the data I am reciving on the other side(central) that is my mobile device(iphone X) with BlueLight application. I am able to find a service and I am reciving some form of hex code,but I don't know how to convert it to wave files or don't know whether it's possible or not .

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          #include <ArduinoBLE.h> #include <mic.h> // microphone library // Microphone Settings #define DEBUG 1 #define SAMPLES 800 mic_config_t mic_config = { .channel_cnt = 1, .sampling_rate = 16000, .buf_size = 1600, .debug_pin = LED_BUILTIN }; NRF52840_ADC_Class Mic(&mic_config); int16_t recording_buf[SAMPLES]; volatile static bool record_ready = false; // Updated UUIDs #define SERVICE_UUID "19B10000-E8F2-537E-4F6C-D104768A1214" #define CHARACTERISTIC_UUID_AUDIO "19B10001-E8F2-537E-4F6C-D104768A1214" // BLE Service and Characteristic BLEService audioService(SERVICE_UUID); // Corrected initialization with explicit value size and fixed length flag BLECharacteristic audioDataCharacteristic(CHARACTERISTIC_UUID_AUDIO, BLERead | BLENotify | BLEWrite, sizeof(recording_buf), true); void setup() { Serial.begin(115200); while (!Serial) delay(10); Serial.println("Initializing microphone..."); Mic.set_callback(audio_rec_callback); if (!Mic.begin()) { Serial.println("Mic initialization failed"); while (1); } Serial.println("Mic initialized."); Serial.println("Initializing BLE..."); if (!BLE.begin()) { Serial.println("Failed to start BLE!"); while (1); } BLE.setLocalName("SCT Audio"); BLE.setAdvertisedService(audioService); audioService.addCharacteristic(audioDataCharacteristic); BLE.addService(audioService); // Corrected writeValue call with explicit casting audioDataCharacteristic.writeValue((uint8_t)0); BLE.advertise(); Serial.println("BLE Peripheral is now advertising"); } void loop() { BLEDevice central = BLE.central(); if (central) { Serial.println("Connected to central device"); while (central.connected()) { if (record_ready) { // Plot the audio data in the Serial Plotter for (int i = 0; i < SAMPLES; i++) { Serial.println(recording_buf[i]); } // Transmit the audio data audioDataCharacteristic.writeValue((uint8_t*)recording_buf, 2 * SAMPLES); Serial.println("Audio data transmitted over BLE"); record_ready = false; } } Serial.println("Disconnected from central device"); } } static void audio_rec_callback(uint16_t *buf, uint32_t buf_len) { static uint32_t idx = 0; for (uint32_t i = 0; i < buf_len; i++) { recording_buf[idx++] = buf[i]; if (idx >= SAMPLES){ idx = 0; record_ready = true; break; } } }

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