First commit

README.md

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+# Sound Event Detection with Edge Impulse
+![Deployed sound recognizer](assets/soundrecognition-with-edge-impulse.gif)
+
+## Overview
+This repository is a companion to the [Sound Recognition with Edge Impulse
+tutorial](https://microchipdeveloper.com/machine-learning:soundrecognition-with-edge-impulse)
+on the Microchip Developer website; it contains the firmware to classify between vacuum cleaner sound and background noise on a [SAME54 Curiosity
+Ultra](https://www.microchip.com/DevelopmentTools/ProductDetails/PartNO/DM320210)
+development board +
+[WM8904](https://www.microchip.com/Developmenttools/ProductDetails/AC328904)
+audio daughterboard, as well as some code for creating a custom processing block with Edge Impulse.
+
+## Benchmarks
+Measured with ``-O2`` compiler optimizations and 120MHz Clock
+- 144kB Flash
+- 76kB RAM
+- 98ms Inference time
+- 96.9% Test set accuracy (see section on [Audio Dataset](#audio-dataset))
+
+## Audio Dataset
+A dataset for vacuum cleaner detection can be downloaded from the [releases page](releases). The data is a selected subset of the [DEMAND](https://zenodo.org/record/1227121#.XRKKxYhKiUk) and [MS-SNSD](https://github.com/microsoft/MS-SNSD), with the vacuum cleaner sounds pulled from MS-SNSD, and the background noise segments (babble noise, air conditioner, and a mix of indoor domestic noise) pulled from both DEMAND and MS-SNSD.
+
+## LogMFE Custom Processing Block
+Code for adding LogMFE audio features to Edge Impulse Studio is located under the [custom-processing-blocks](custom-processing-blocks) directory.

custom-processing-blocks/LICENSE

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+Apache License
+Version 2.0, January 2004
+http://www.apache.org/licenses/
+
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custom-processing-blocks/README.md

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+# Custom Processing Blocks
+This directory contains the code to host a server for extracting LogMFE and LogSpectrogram features with Edge Impulse Studio. The details are further explained in the sound recognition tutorial on [microchip developer](https://microchipdeveloper.com/machine-learning:soundrecognition-with-edge-impulse).
+
+Follow [this guide](https://docs.edgeimpulse.com/docs/custom-blocks) for instructions on how to integrate this code into Edge Impulse Studio as custom processing blocks.
+
+Note this code is a fork of the official processing blocks maintained by Edge Impulse at [this repository](https://github.com/edgeimpulse/processing-blocks).

custom-processing-blocks/logmfe/Dockerfile

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+# syntax = docker/dockerfile:experimental
+FROM python:3.7.5-stretch
+
+WORKDIR /app
+
+# Python dependencies
+COPY requirements-blocks.txt ./
+RUN pip3 --no-cache-dir install -r requirements-blocks.txt
+
+COPY third_party /third_party
+COPY . ./
+
+EXPOSE 4446
+
+CMD python3 -u dsp-server.py

custom-processing-blocks/logmfe/__init__.py

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+from .dsp import generate_features

custom-processing-blocks/logmfe/dsp-server.py

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+# This is a generic Edge Impulse DSP server in Python
+# You probably don't need to change this file.
+
+import sys, importlib, os, socket, json, math, traceback
+from http.server import HTTPServer, BaseHTTPRequestHandler
+from socketserver import ThreadingMixIn
+import threading
+from urllib.parse import urlparse, parse_qs
+import traceback
+import logging
+import numpy as np
+from dsp import generate_features
+
+def get_params(self):
+    with open('parameters.json', 'r') as f:
+        return json.loads(f.read())
+
+def single_req(self, fn, body):
+    if (not body['features'] or len(body['features']) == 0):
+        raise ValueError('Missing "features" in body')
+    if (not 'params' in body):
+        raise ValueError('Missing "params" in body')
+    if (not 'sampling_freq' in body):
+        raise ValueError('Missing "sampling_freq" in body')
+    if (not 'draw_graphs' in body):
+        raise ValueError('Missing "draw_graphs" in body')
+
+    args = {
+        'draw_graphs': body['draw_graphs'],
+        'raw_data': np.array(body['features']),
+        'axes': np.array(body['axes']),
+        'sampling_freq': body['sampling_freq'],
+        'implementation_version': body['implementation_version']
+    }
+
+    for param_key in body['params'].keys():
+        args[param_key] = body['params'][param_key]
+
+    processed = fn(**args)
+    if (isinstance(processed['features'], np.ndarray)):
+        processed['features'] = processed['features'].tolist()
+
+    body = json.dumps(processed)
+
+    self.send_response(200)
+    self.send_header('Content-Type', 'application/json')
+    self.end_headers()
+    self.wfile.write(body.encode())
+
+def batch_req(self, fn, body):
+    if (not body['features'] or len(body['features']) == 0):
+        raise ValueError('Missing "features" in body')
+    if (not 'params' in body):
+        raise ValueError('Missing "params" in body')
+    if (not 'sampling_freq' in body):
+        raise ValueError('Missing "sampling_freq" in body')
+
+    base_args = {
+        'draw_graphs': False,
+        'axes': np.array(body['axes']),
+        'sampling_freq': body['sampling_freq'],
+        'implementation_version': body['implementation_version']
+    }
+
+    for param_key in body['params'].keys():
+        base_args[param_key] = body['params'][param_key]
+
+    total = 0
+    features = []
+    labels = []
+    output_config = None
+
+    for example in body['features']:
+        args = dict(base_args)
+        args['raw_data'] = np.array(example)
+        f = fn(**args)
+        if (isinstance(f['features'], np.ndarray)):
+            features.append(f['features'].tolist())
+        else:
+            features.append(f['features'])
+
+        if total == 0:
+            if ('labels' in f):
+                labels = f['labels']
+            if ('output_config' in f):
+                output_config = f['output_config']
+
+        total += 1
+
+    body = json.dumps({
+        'success': True,
+        'features': features,
+        'labels': labels,
+        'output_config': output_config
+    })
+
+    self.send_response(200)
+    self.send_header('Content-Type', 'application/json')
+    self.end_headers()
+    self.wfile.write(body.encode())
+
+class Handler(BaseHTTPRequestHandler):
+    def do_GET(self):
+        url = urlparse(self.path)
+        params = get_params(self)
+
+        if (url.path == '/'):
+            self.send_response(200)
+            self.send_header('Content-Type', 'text/plain')
+            self.end_headers()
+            self.wfile.write(('Edge Impulse DSP block: ' + params['info']['title'] + ' by ' +
+                params['info']['author']).encode())
+
+        elif (url.path == '/parameters'):
+            self.send_response(200)
+            self.send_header('Content-Type', 'application/json')
+            self.end_headers()
+            params['version'] = 1
+            self.wfile.write(json.dumps(params).encode())
+
+        else:
+            self.send_response(404)
+            self.send_header('Content-Type', 'text/plain')
+            self.end_headers()
+            self.wfile.write(b'Invalid path ' + self.path.encode() + b'\n')
+
+    def do_POST(self):
+        url = urlparse(self.path)
+        try:
+            if (url.path == '/run'):
+                content_len = int(self.headers.get('Content-Length'))
+                post_body = self.rfile.read(content_len)
+                body = json.loads(post_body.decode('utf-8'))
+                single_req(self, generate_features, body)
+
+            elif (url.path == '/batch'):
+                content_len = int(self.headers.get('Content-Length'))
+                post_body = self.rfile.read(content_len)
+                body = json.loads(post_body.decode('utf-8'))
+                batch_req(self, generate_features, body)
+
+            else:
+                self.send_response(404)
+                self.send_header('Content-Type', 'text/plain')
+                self.end_headers()
+                self.wfile.write(b'Invalid path ' + self.path.encode() + b'\n')
+
+
+        except Exception as e:
+            print('Failed to handle request', e, traceback.format_exc())
+            self.send_response(200)
+            self.send_header('Content-Type', 'application/json')
+            self.end_headers()
+            self.wfile.write(json.dumps({ 'success': False, 'error': str(e) }).encode())
+
+    def log_message(self, format, *args):
+        return
+
+class ThreadingSimpleServer(ThreadingMixIn, HTTPServer):
+    pass
+
+def run():
+    host = '0.0.0.0' if not 'HOST' in os.environ else os.environ['HOST']
+    port = 4446 if not 'PORT' in os.environ else int(os.environ['PORT'])
+
+    server = ThreadingSimpleServer((host, port), Handler)
+    print('Listening on host', host, 'port', port)
+    server.serve_forever()
+
+if __name__ == '__main__':
+    run()