回复了主题帖： 【Follow me第二季第4期】基于 Edge Impulse 的语音识别

lugl4313820 发表于 2024-12-30 22:25 咱们这个语音识别，是要联网？还是把算法下载到本地就可以识别了？ 把算法下载到了单片机上

回复了主题帖： 【Follow me第二季第4期】任务汇总

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回复了主题帖： 【Follow me第二季第4期】任务二 学习IMU基础知识，通过串口打印六轴原始数据

补充一下演示视频 [localvideo]9de5480931303ec0416d695adf902631[/localvideo]  

加入了学习《Follow me第二季第4期 Arduino? Nano RP2040 Connect活动项目总结》，观看 Follow me第二季第4期项目总结

回复了主题帖： 【Follow me第二季第4期】任务汇总

【Follow me第二季第4期】基于 Edge Impulse 的语音识别 https://bbs.eeworld.com.cn/thread-1302947-1-1.html

发表了主题帖： 【Follow me第二季第4期】基于 Edge Impulse 的语音识别

本帖最后由 eew_uscYT9 于 2024-12-28 23:23 编辑 本项目教你如何做出自己的语音识别 本项目的大体流程  我的语音识别是识别开灯和关灯两个声音，听到对应的声音就对板子上的灯进行开和关   https://studio.edgeimpulse.com/ 先去这个网站注册好账号 然后创建一个新的项目     输入项目名字，其他保存不动 接着到了数据的采集，我通过对Edge Impulse的文档查阅，发现可以用Arduino Nano主板进行数据的采集，其他的数据采集方式有手机、Edge Impulse CLI（该方式比较复杂） 先下载Edge Impulse准备好的固件 然后把该固件下载到nano板子上 接着点左边的data acquisition   点击下面所指的图标就能进行数据的采集，数据分为训练数据集和测试数据集，一般是8、2开，   数据采集完之后点击creat impulse进行训练的设置，如下面所示，设置完之后点击save impulse   点击mfcc进行参数的设置，直接默认设置，然后保存参数   然后点击上方的generate features进行生成特征 ，根据feature explorer能够看出我的数据区分度还是挺高的   接着点击classifier 进行训练，参数都可以用默认设置   点击model  testing进行模型测试 我的模型训练还是可以的  接下来进行模型的部署，我们现在arduino，然后进行build，把生成的文件下载下来，打开arduino ide进行添加   我的代码如下 /* Edge Impulse ingestion SDK * Copyright (c) 2022 EdgeImpulse Inc. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * */ // If your target is limited in memory remove this macro to save 10K RAM #define EIDSP_QUANTIZE_FILTERBANK 0 /* ** NOTE: If you run into TFLite arena allocation issue. ** ** This may be due to may dynamic memory fragmentation. ** Try defining "-DEI_CLASSIFIER_ALLOCATION_STATIC" in boards.local.txt (create ** if it doesn't exist) and copy this file to ** `<ARDUINO_CORE_INSTALL_PATH>/arduino/hardware/<mbed_core>/<core_version>/`. ** ** See ** (https://support.arduino.cc/hc/en-us/articles/360012076960-Where-are-the-installed-cores-located-) ** to find where Arduino installs cores on your machine. ** ** If the problem persists then there's not enough memory for this model and application. */ /* Includes ---------------------------------------------------------------- */ #include <rp2040_inferencing.h> #include <PDM.h> #include "WiFiNINA.h" #define led1 LEDB #define led2 LEDG #define led3 LEDR /** Audio buffers, pointers and selectors */ typedef struct { int16_t *buffer; uint8_t buf_ready; uint32_t buf_count; uint32_t n_samples; } inference_t; static inference_t inference; static signed short sampleBuffer[2048]; static bool debug_nn = false; // Set this to true to see e.g. features generated from the raw signal static volatile bool record_ready = false; /** * [url=home.php?mod=space&uid=159083]@brief[/url] Arduino setup function */ void setup() { // put your setup code here, to run once: Serial.begin(115200); pinMode(led3, OUTPUT); pinMode(led2, OUTPUT); // comment out the below line to cancel the wait for USB connection (needed for native USB) while (!Serial); Serial.println("Edge Impulse Inferencing Demo"); // summary of inferencing settings (from model_metadata.h) ei_printf("Inferencing settings:\n"); ei_printf("\tInterval: "); ei_printf_float((float)EI_CLASSIFIER_INTERVAL_MS); ei_printf(" ms.\n"); ei_printf("\tFrame size: %d\n", EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE); ei_printf("\tSample length: %d ms.\n", EI_CLASSIFIER_RAW_SAMPLE_COUNT / 16); ei_printf("\tNo. of classes: %d\n", sizeof(ei_classifier_inferencing_categories) / sizeof(ei_classifier_inferencing_categories[0])); if (microphone_inference_start(EI_CLASSIFIER_RAW_SAMPLE_COUNT) == false) { ei_printf("ERR: Could not allocate audio buffer (size %d), this could be due to the window length of your model\r\n", EI_CLASSIFIER_RAW_SAMPLE_COUNT); return; } } /** * @brief Arduino main function. Runs the inferencing loop. */ void loop() { ei_printf("Starting inferencing in 2 seconds...\n"); delay(2000); ei_printf("Recording...\n"); bool m = microphone_inference_record(); if (!m) { ei_printf("ERR: Failed to record audio...\n"); return; } ei_printf("Recording done\n"); signal_t signal; signal.total_length = EI_CLASSIFIER_RAW_SAMPLE_COUNT; signal.get_data = &microphone_audio_signal_get_data; ei_impulse_result_t result = { 0 }; EI_IMPULSE_ERROR res = run_classifier_continuous(&signal, &result, debug_nn); if (res != EI_IMPULSE_OK) { ei_printf("ERR: Failed to run classifier (%d)\n", res); return; } // print inference return code ei_printf("run_classifier returned: %d\r\n", res); print_inference_result(result); //ei_printf(" %s: ", ei_classifier_inferencing_categories[i]); } /** * @brief PDM buffer full callback * Copy audio data to app buffers */ static void pdm_data_ready_inference_callback(void) { int bytesAvailable = PDM.available(); // read into the sample buffer int bytesRead = PDM.read((char *)&sampleBuffer[0], bytesAvailable); if ((inference.buf_ready == 0) && (record_ready == true)) { for(int i = 0; i < bytesRead>>1; i++) { inference.buffer[inference.buf_count++] = sampleBuffer[i]; if(inference.buf_count >= inference.n_samples) { inference.buf_count = 0; inference.buf_ready = 1; break; } } } } /** * @brief Init inferencing struct and setup/start PDM * * @param[in] n_samples The n samples * * [url=home.php?mod=space&uid=784970]@return[/url] { description_of_the_return_value } */ static bool microphone_inference_start(uint32_t n_samples) { inference.buffer = (int16_t *)malloc(n_samples * sizeof(int16_t)); if(inference.buffer == NULL) { return false; } inference.buf_count = 0; inference.n_samples = n_samples; inference.buf_ready = 0; // configure the data receive callback PDM.onReceive(pdm_data_ready_inference_callback); PDM.setBufferSize(2048); delay(250); // initialize PDM with: // - one channel (mono mode) if (!PDM.begin(1, EI_CLASSIFIER_FREQUENCY)) { ei_printf("ERR: Failed to start PDM!"); microphone_inference_end(); return false; } // optionally set the gain, defaults to 24 // Note: values >=52 not supported //PDM.setGain(40); return true; } /** * @brief Wait on new data * * @return True when finished */ static bool microphone_inference_record(void) { bool ret = true; record_ready = true; while (inference.buf_ready == 0) { delay(10); } inference.buf_ready = 0; record_ready = false; return ret; } /** * Get raw audio signal data */ static int microphone_audio_signal_get_data(size_t offset, size_t length, float *out_ptr) { numpy::int16_to_float(&inference.buffer[offset], out_ptr, length); return 0; } /** * @brief Stop PDM and release buffers */ static void microphone_inference_end(void) { PDM.end(); ei_free(inference.buffer); } void print_inference_result(ei_impulse_result_t result) { // Print how long it took to perform inference ei_printf("Timing: DSP %d ms, inference %d ms, anomaly %d ms\r\n", result.timing.dsp, result.timing.classification, result.timing.anomaly); ei_printf("Predictions:\r\n"); for (uint16_t i = 0; i < EI_CLASSIFIER_LABEL_COUNT; i++) { if(result.classification[i].value>0.7) { switch (i) { case 0: digitalWrite(led3, LOW); break; case 1: digitalWrite(led3, HIGH); break; default: digitalWrite(led2, LOW); } } ei_printf(" %s: ", ei_classifier_inferencing_categories[i]); ei_printf("%.5f\r\n", result.classification[i].value); } // Print anomaly result (if it exists) #if EI_CLASSIFIER_HAS_ANOMALY == 1 ei_printf("Anomaly prediction: %.3f\r\n", result.anomaly); #endif } #if !defined(EI_CLASSIFIER_SENSOR) || EI_CLASSIFIER_SENSOR != EI_CLASSIFIER_SENSOR_MICROPHONE #error "Invalid model for current sensor." #endif   演示视频 [localvideo]d8177a49fca202d9ad2e38951857c1aa[/localvideo]      

回复了主题帖： 【Follow me第二季第4期】任务三 学调试PDM麦克风，通过串口打印收音数据和音频波形

本帖最后由 eew_uscYT9 于 2024-12-29 23:27 编辑 [localvideo]2d950ac4b69a1ed58dc86b1ceae90a16[/localvideo] 对应的视频展示 还有一个和pico2联动，通过打响指能控制nano板子和pico2板子的灯进行变化 [localvideo]2d51811098e155c28760aee1191550e6[/localvideo]  

加入了学习《【Follow me第二季第4期】ARDUINO NANO RP2040 CONNECT》，观看 Arduino NANO RP2040演示合集

回复了主题帖： 【Follow me第二季第4期】任务三 学调试PDM麦克风，通过串口打印收音数据和音频波形

Jacktang 发表于 2024-12-24 07:45 结果输出的曲线为什么是下降的 截的是做完之后的数据

加入了学习《Arduino? Nano RP2040 Connect 任务视频》，观看 PDM 数据打印及音频波形

发表了主题帖： 【Follow me第二季第4期】任务汇总

本帖最后由 eew_uscYT9 于 2025-1-11 21:43 编辑 视频 【Follow me第二季第4期】-EEWORLD大学堂   必做任务一：搭建环境并开启第一步Blink三色LED / 串口打印Hello DigiKey & EEWorld！；   【Follow me第二季第4期】任务一Blink、串口打印 https://bbs.eeworld.com.cn/thread-1301039-1-1.html 必做任务二：学习IMU基础知识，调试IMU传感器，通过串口打印六轴原始数据；   【Follow me第二季第4期】任务二 学习IMU基础知识，通过串口打印六轴原始数据 https://bbs.eeworld.com.cn/thread-1301142-1-1.html 必做任务三：学习PDM麦克风技术知识，调试PDM麦克风，通过串口打印收音数据和音频波形。 通过打响指进行灯的控制，也控制pico2的灯的亮灭   具体流程如下   【Follow me第二季第4期】任务三 学调试PDM麦克风，通过串口打印收音数据和音频波形 https://bbs.eeworld.com.cn/thread-1301144-1-1.html   发挥任务： 【Follow me第二季第4期】基于 Edge Impulse 的语音识别 https://bbs.eeworld.com.cn/thread-1302947-1-1.html   心得体会 通过这次我学习到了IMU的使用、麦克风的使用、了解到了音频的波形，也学习到了怎么样进行机器学习，并且自己也部署了一个可以识别命令的进行开关灯的小demo 最后非常感谢EEWorld和得捷电子举办的活动，这次活动让我收获了许多知识，希望能多出一点有关ai方向，让我们了解前沿，与前沿接轨   代码 https://download.eeworld.com.cn/detail/eew_uscYT9/635471  

发表了主题帖： 【Follow me第二季第4期】任务三 学调试PDM麦克风，通过串口打印收音数据和音频波形

本帖最后由 eew_uscYT9 于 2024-12-23 17:26 编辑 硬件部分 ST MP34DT06JTR MEMS麦克风   ▪ AOP = 122.5 dBSPL   ▪ 64 dB信噪比   ▪ 全向灵敏度   ▪ -26 dBFS ± 1 dB灵敏度   硬件连接   代码部分 先安装PDM库 #include <WiFiNINA.h> #include <PDM.h> bool LED_SWITCH = false; // default number of output channels static const char channels = 1; // default PCM output frequency static const int frequency = 20000; // Buffer to read samples into, each sample is 16-bits short sampleBuffer[512]; // Number of audio samples read volatile int samplesRead; void setup() { Serial.begin(115200); pinMode(LEDB, OUTPUT); while (!Serial); // Configure the data receive callback PDM.onReceive(onPDMdata); // Optionally set the gain // Defaults to 20 on the BLE Sense and -10 on the Portenta Vision Shields // PDM.setGain(30); // Initialize PDM with: // - one channel (mono mode) // - a 16 kHz sample rate for the Arduino Nano 33 BLE Sense // - a 32 kHz or 64 kHz sample rate for the Arduino Portenta Vision Shields if (!PDM.begin(channels, frequency)) { Serial.println("Failed to start PDM!"); while (1); } } void loop() { // Wait for samples to be read if (samplesRead) { // Print samples to the serial monitor or plotter for (int i = 0; i < samplesRead; i++) { if (channels == 2) { Serial.print("L:"); Serial.print(sampleBuffer[i]); Serial.print(" R:"); i++; } Serial.println(sampleBuffer[i]); if (sampleBuffer[i] > 10000 || sampleBuffer[i] <= -10000) { LED_SWITCH = !LED_SWITCH; if (LED_SWITCH) { Serial.println(); digitalWrite(LEDR, HIGH); Serial.println("ON!"); Serial.println(); delay(1000); } else { Serial.println(); digitalWrite(LEDR, LOW); Serial.println("OFF!"); Serial.println(); delay(1000); } } } // Clear the read count samplesRead = 0; } } /** Callback function to process the data from the PDM microphone. NOTE: This callback is executed as part of an ISR. Therefore using `Serial` to print messages inside this function isn't supported. * */ void onPDMdata() { // Query the number of available bytes int bytesAvailable = PDM.available(); // Read into the sample buffer PDM.read(sampleBuffer, bytesAvailable); // 16-bit, 2 bytes per sample samplesRead = bytesAvailable / 2; }   现象是打一个响指就会让红灯亮，再打一个就会关闭红灯     输出的数据如下      

加入了学习《直播回放: DigiKey FollowMe 第二季 第4期 Arduino Nano RP2040 Connect 任务讲解》，观看 Arduino Nano RP2040 Connect 任务讲解

发表了主题帖： 【Follow me第二季第4期】任务二 学习IMU基础知识，通过串口打印六轴原始数据

本帖最后由 eew_uscYT9 于 2024-12-6 20:00 编辑 Nano RP2040 Connect Cheat Sheet | Arduino Documentation   板子的arduino教程   ST LSM6DSOXTR 6轴惯性测量单元（IMU）   ▪ 3D陀螺仪         • ±2/±4/±8/±16 g全量程   ▪ 3D加速度计         • ±125/±250/±500/±1000/±2000 dps全量程   ▪ 高级计步器、步态检测器和步数计数器   ▪ 运动检测、倾斜检测   ▪ 标准中断：自由落体、唤醒、6D/4D方向、单击和双击   ▪ 可编程有限状态机：加速度计、陀螺仪和外部传感器   ▪ 机器学习核心   ▪ 嵌入式温度传感器     加速度的各个方向   陀螺仪的各个方向   实现步骤 1先在arduino上安装Arduino_LSM6DSOX库   相关的api     代码如下 #include <Arduino_LSM6DSOX.h> float Ax, Ay, Az; float Gx, Gy, Gz; void setup() { Serial.begin(9600); while(!Serial); if (!IMU.begin()) { Serial.println("Failed to initialize IMU!"); while (1); } Serial.print("Accelerometer sample rate = "); Serial.print(IMU.accelerationSampleRate()); Serial.println("Hz"); Serial.println(); Serial.print("Gyroscope sample rate = "); Serial.print(IMU.gyroscopeSampleRate()); Serial.println("Hz"); Serial.println(); } void loop() { if (IMU.accelerationAvailable()) { if (IMU.gyroscopeAvailable()) { IMU.readAcceleration(Ax, Ay, Az); IMU.readGyroscope(Gx, Gy, Gz); Serial.println("data: "); Serial.print(Ax); Serial.print('\t'); Serial.print(Ay); Serial.print('\t'); Serial.println(Az); Serial.print(Gx); Serial.print('\t'); Serial.print(Gy); Serial.print('\t'); Serial.println(Gz); Serial.println(); } } delay(250); }   串口数据输出如下   想解算姿态，但是不知道该怎么做，求教

回复了主题帖： 【Follow me第二季第4期】任务一Blink、串口打印

Jacktang 发表于 2024-12-6 07:29 就是控制一下三个灯的亮和灭 对的

发表了主题帖： 【Follow me第二季第4期】任务一Blink、串口打印

本帖最后由 eew_uscYT9 于 2024-12-6 23:40 编辑 点灯启动   RGB连接方式   有原理图可以看出，低电平点亮 代码如下 #include "WiFiNINA.h" #define led1 LEDB #define led2 LEDG #define led3 LEDR void setup() { Serial.begin(9600); pinMode(led1, OUTPUT); pinMode(led2, OUTPUT); pinMode(led3, OUTPUT); } void loop() { Serial.println("Hello DigiKey & EEWorld！"); // Red digitalWrite(led1, LOW); digitalWrite(led2, HIGH); digitalWrite(led3, HIGH); delay(500); // Green digitalWrite(led1, HIGH); digitalWrite(led2, LOW); digitalWrite(led3, HIGH); delay(500); // Blue digitalWrite(led1, HIGH); digitalWrite(led2, HIGH); digitalWrite(led3, LOW); delay(500); }   顺带打印了"Hello DigiKey & EEWorld！"  

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