Bump

mobile/src/main/AndroidManifest.xml

@@ -1,6 +1,10 @@
 <?xml version="1.0" encoding="utf-8"?>
 <manifest xmlns:android="http://schemas.android.com/apk/res/android"
-    xmlns:tools="http://schemas.android.com/tools">
+    xmlns:tools="http://schemas.android.com/tools" >
+
+    <uses-permission android:name="android.permission.INTERNET"></uses-permission>
+    <uses-permission android:name="android.permission.ACCESS_COARSE_LOCATION"></uses-permission>
+    <uses-permission android:name="android.permission.ACCESS_FINE_LOCATION"></uses-permission>
 
     <application
         android:allowBackup="true"
@@ -11,16 +15,31 @@
         android:roundIcon="@mipmap/ic_launcher_round"
         android:supportsRtl="true"
         android:theme="@style/Theme.Identify"
-        tools:targetApi="31">
+        tools:targetApi="31" >
+
         <activity
             android:name=".MainActivity"
-            android:exported="true">
+            android:exported="true" >
             <intent-filter>
                 <action android:name="android.intent.action.MAIN" />
 
                 <category android:name="android.intent.category.LAUNCHER" />
             </intent-filter>
         </activity>
+
+        <service
+            android:name=".MessageListenerService"
+            android:enabled="true"
+            android:exported="true" >
+            <intent-filter>
+                <action android:name="com.google.android.gms.wearable.MESSAGE_RECEIVED" />
+
+                <data
+                    android:host="*"
+                    android:pathPrefix="/audio"
+                    android:scheme="wear" />
+            </intent-filter>
+        </service>
     </application>
 
 </manifest>

mobile/src/main/java/com/birdsounds/identify/Downloader.java

@@ -0,0 +1,189 @@
+package com.birdsounds.identify;
+import android.app.Activity;
+import android.content.Context;
+import android.util.Log;
+import android.view.View;
+import android.widget.Toast;
+
+import java.io.BufferedInputStream;
+import java.io.File;
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.io.InputStream;
+import java.math.BigInteger;
+import java.net.URL;
+import java.net.URLConnection;
+import java.nio.file.Files;
+import java.nio.file.Paths;
+import java.security.MessageDigest;
+import java.security.NoSuchAlgorithmException;
+
+
+@SuppressWarnings("ResultOfMethodCallIgnored")
+public class Downloader {
+    static final String modelFILE = "model.tflite";
+    static final String metaModelFILE = "metaModel.tflite";
+    static final String modelURL = "https://raw.githubusercontent.com/woheller69/whoBIRD-TFlite/master/BirdNET_GLOBAL_6K_V2.4_Model_FP16.tflite";
+    static final String model32URL = "https://raw.githubusercontent.com/woheller69/whoBIRD-TFlite/master/BirdNET_GLOBAL_6K_V2.4_Model_FP32.tflite";
+    static final String metaModelURL = "https://raw.githubusercontent.com/woheller69/whoBIRD-TFlite/master/BirdNET_GLOBAL_6K_V2.4_MData_Model_FP16.tflite";
+    static final String modelMD5 = "b1c981fe261910b473b9b7eec9ebcd4e";
+    static final String model32MD5 = "6c7c42106e56550fc8563adb31bc120e";
+    static final String metaModelMD5 ="f1a078ae0f244a1ff5a8f1ccb645c805";
+
+    public static boolean checkModels(final Activity activity) {
+        File modelFile = new File(activity.getDir("filesdir", Context.MODE_PRIVATE) + "/" + modelFILE);
+        File metaModelFile = new File(activity.getDir("filesdir", Context.MODE_PRIVATE) + "/" + metaModelFILE);
+        String calcModelMD5 = "";
+        String calcMetaModelMD5 = "";
+        if (modelFile.exists()) {
+            try {
+                byte[] data = Files.readAllBytes(Paths.get(modelFile.getPath()));
+                byte[] hash = MessageDigest.getInstance("MD5").digest(data);
+                calcModelMD5 = new BigInteger(1, hash).toString(16);
+            } catch (IOException | NoSuchAlgorithmException e) {
+                throw new RuntimeException(e);
+            }
+        }
+        if (metaModelFile.exists()) {
+            try {
+                byte[] data = Files.readAllBytes(Paths.get(metaModelFile.getPath()));
+                byte[] hash = MessageDigest.getInstance("MD5").digest(data);
+                calcMetaModelMD5 = new BigInteger(1, hash).toString(16);
+            } catch (IOException | NoSuchAlgorithmException e) {
+                throw new RuntimeException(e);
+            }
+        }
+
+        if (modelFile.exists() && !(calcModelMD5.equals(modelMD5) || calcModelMD5.equals(model32MD5))) modelFile.delete();
+        if (metaModelFile.exists() && !calcMetaModelMD5.equals(metaModelMD5)) metaModelFile.delete();
+
+        return (calcModelMD5.equals(modelMD5) || calcModelMD5.equals(model32MD5)) && calcMetaModelMD5.equals(metaModelMD5);
+    }
+
+    public static void downloadModels(final Activity activity) {
+        File modelFile = new File(activity.getDir("filesdir", Context.MODE_PRIVATE) + "/" + modelFILE);
+        Log.d("Heyy","Model file checking");
+        if (!modelFile.exists()) {
+            Log.d("whoBIRD", "model file does not exist");
+            Thread thread = new Thread(() -> {
+                try {
+                    URL url;
+                    if (false) url = new URL(model32URL);
+                    else url = new URL(modelURL);
+
+                    Log.d("whoBIRD", "Download model");
+
+                    URLConnection ucon = url.openConnection();
+                    ucon.setReadTimeout(5000);
+                    ucon.setConnectTimeout(10000);
+
+                    InputStream is = ucon.getInputStream();
+                    BufferedInputStream inStream = new BufferedInputStream(is, 1024 * 5);
+
+                    modelFile.createNewFile();
+
+                    FileOutputStream outStream = new FileOutputStream(modelFile);
+                    byte[] buff = new byte[5 * 1024];
+
+                    int len;
+                    while ((len = inStream.read(buff)) != -1) {
+                        outStream.write(buff, 0, len);
+                    }
+                    outStream.flush();
+                    outStream.close();
+                    inStream.close();
+
+                    String calcModelMD5="";
+                    if (modelFile.exists()) {
+                        byte[] data = Files.readAllBytes(Paths.get(modelFile.getPath()));
+                        byte[] hash = MessageDigest.getInstance("MD5").digest(data);
+                        calcModelMD5 = new BigInteger(1, hash).toString(16);
+                    } else {
+                        throw new IOException();  //throw exception if there is no modelFile at this point
+                    }
+
+                    if (!(calcModelMD5.equals(modelMD5) || calcModelMD5.equals(model32MD5) )){
+                        modelFile.delete();
+                        activity.runOnUiThread(() -> {
+                            Toast.makeText(activity, activity.getResources().getString(R.string.error_download), Toast.LENGTH_SHORT).show();
+                        });
+                    } else {
+                        activity.runOnUiThread(() -> {
+
+                        });
+                    }
+                } catch (NoSuchAlgorithmException | IOException i) {
+                    activity.runOnUiThread(() -> Toast.makeText(activity, activity.getResources().getString(R.string.error_download), Toast.LENGTH_SHORT).show());
+                    modelFile.delete();
+                    Log.w("whoBIRD", activity.getResources().getString(R.string.error_download), i);
+                }
+            });
+            thread.start();
+        } else {
+            Log.d("whoBIRD","model exists");
+            activity.runOnUiThread(() -> {
+
+            });
+        }
+
+        File metaModelFile = new File(activity.getDir("filesdir", Context.MODE_PRIVATE) + "/" + metaModelFILE);
+        if (!metaModelFile.exists()) {
+            Log.d("whoBIRD", "meta model file does not exist");
+            Thread thread = new Thread(() -> {
+                try {
+                    URL url = new URL(metaModelURL);
+                    Log.d("whoBIRD", "Download meta model");
+
+                    URLConnection ucon = url.openConnection();
+                    ucon.setReadTimeout(5000);
+                    ucon.setConnectTimeout(10000);
+
+                    InputStream is = ucon.getInputStream();
+                    BufferedInputStream inStream = new BufferedInputStream(is, 1024 * 5);
+
+                    metaModelFile.createNewFile();
+
+                    FileOutputStream outStream = new FileOutputStream(metaModelFile);
+                    byte[] buff = new byte[5 * 1024];
+
+                    int len;
+                    while ((len = inStream.read(buff)) != -1) {
+                        outStream.write(buff, 0, len);
+                    }
+                    outStream.flush();
+                    outStream.close();
+                    inStream.close();
+
+                    String calcMetaModelMD5="";
+                    if (metaModelFile.exists()) {
+                        byte[] data = Files.readAllBytes(Paths.get(metaModelFile.getPath()));
+                        byte[] hash = MessageDigest.getInstance("MD5").digest(data);
+                        calcMetaModelMD5 = new BigInteger(1, hash).toString(16);
+                    } else {
+                        throw new IOException();  //throw exception if there is no modelFile at this point
+                    }
+
+                    if (!calcMetaModelMD5.equals(metaModelMD5)){
+                        metaModelFile.delete();
+                        activity.runOnUiThread(() -> {
+                            Toast.makeText(activity, activity.getResources().getString(R.string.error_download), Toast.LENGTH_SHORT).show();
+                        });
+                    } else {
+                        activity.runOnUiThread(() -> {
+                               });
+                    }
+                } catch (NoSuchAlgorithmException | IOException i) {
+                    activity.runOnUiThread(() -> Toast.makeText(activity, activity.getResources().getString(R.string.error_download), Toast.LENGTH_SHORT).show());
+                    metaModelFile.delete();
+                    Log.w("whoBIRD", activity.getResources().getString(R.string.error_download), i);
+                }
+            });
+            thread.start();
+        } else {
+            Log.d("whoBIRD", "meta file exists");
+            activity.runOnUiThread(() -> {
+             });
+        }
+
+    }
+}

mobile/src/main/java/com/birdsounds/identify/Location.java

@@ -0,0 +1,59 @@
+package com.birdsounds.identify;
+
+import android.Manifest;
+import android.content.Context;
+import android.content.pm.PackageManager;
+import android.location.LocationListener;
+import android.location.LocationManager;
+import android.os.Bundle;
+import android.widget.Toast;
+
+import androidx.core.app.ActivityCompat;
+
+public class Location {
+
+    private static LocationListener locationListenerGPS;
+
+    static void stopLocation(Context context){
+        LocationManager locationManager = (LocationManager) context.getSystemService(Context.LOCATION_SERVICE);
+        if (locationListenerGPS!=null) locationManager.removeUpdates(locationListenerGPS);
+        locationListenerGPS=null;
+    }
+
+    static void requestLocation(Context context, SoundClassifier soundClassifier) {
+        if (ActivityCompat.checkSelfPermission(context, Manifest.permission.ACCESS_COARSE_LOCATION) == PackageManager.PERMISSION_GRANTED && checkLocationProvider(context)) {
+            LocationManager locationManager = (LocationManager) context.getSystemService(Context.LOCATION_SERVICE);
+            if (locationListenerGPS==null) locationListenerGPS = new LocationListener() {
+                @Override
+                public void onLocationChanged(android.location.Location location) {
+                    soundClassifier.runMetaInterpreter(location);
+                }
+
+                @Deprecated
+                @Override
+                public void onStatusChanged(String provider, int status, Bundle extras) {
+                }
+
+                @Override
+                public void onProviderEnabled(String provider) {
+                }
+
+                @Override
+                public void onProviderDisabled(String provider) {
+                }
+            };
+            locationManager.requestLocationUpdates(LocationManager.GPS_PROVIDER, 60000, 0, locationListenerGPS);
+        }
+    }
+
+    public static boolean checkLocationProvider(Context context) {
+        LocationManager locationManager = (LocationManager) context.getSystemService(Context.LOCATION_SERVICE);
+        if (!locationManager.isProviderEnabled(LocationManager.GPS_PROVIDER)){
+            Toast.makeText(context, "Error no GPS", Toast.LENGTH_SHORT).show();
+            return false;
+        } else {
+            return true;
+        }
+    }
+
+}

mobile/src/main/java/com/birdsounds/identify/MainActivity.kt

@@ -1,20 +1,57 @@
 package com.birdsounds.identify
 
+import android.content.pm.PackageManager
 import android.os.Bundle
+import android.Manifest
+import android.util.Log
 import androidx.activity.enableEdgeToEdge
 import androidx.appcompat.app.AppCompatActivity
+import androidx.core.content.ContextCompat
 import androidx.core.view.ViewCompat
 import androidx.core.view.WindowInsetsCompat
+import com.google.android.gms.wearable.ChannelClient
+import com.google.android.gms.wearable.Wearable
 
 class MainActivity : AppCompatActivity() {
+    private lateinit var soundClassifier: SoundClassifier
+
     override fun onCreate(savedInstanceState: Bundle?) {
         super.onCreate(savedInstanceState)
         enableEdgeToEdge()
         setContentView(R.layout.activity_main)
+        Wearable.getChannelClient(this.applicationContext)
+            .registerChannelCallback(object : ChannelClient.ChannelCallback() {
+                override fun onChannelOpened(channel: ChannelClient.Channel) {
+                    super.onChannelOpened(channel)
+                    Log.d("HEY", "onChannelOpened")
+                }
+            }
+            )
+        Downloader.downloadModels(this);
+        requestPermissions();
+        soundClassifier = SoundClassifier(this, SoundClassifier.Options())
+        Location.requestLocation(this, soundClassifier)
         ViewCompat.setOnApplyWindowInsetsListener(findViewById(R.id.main)) { v, insets ->
             val systemBars = insets.getInsets(WindowInsetsCompat.Type.systemBars())
             v.setPadding(systemBars.left, systemBars.top, systemBars.right, systemBars.bottom)
             insets
         }
     }
+    companion object {
+        const val REQUEST_PERMISSIONS = 1337
+    }
+
+    private fun requestPermissions() {
+        val perms = mutableListOf<String>()
+
+        if (ContextCompat.checkSelfPermission(
+                this,
+                Manifest.permission.ACCESS_COARSE_LOCATION
+            ) != PackageManager.PERMISSION_GRANTED
+        ) {
+            perms.add(Manifest.permission.ACCESS_COARSE_LOCATION)
+            perms.add(Manifest.permission.ACCESS_FINE_LOCATION)
+        }
+        if (!perms.isEmpty()) requestPermissions(perms.toTypedArray(), REQUEST_PERMISSIONS)
+    }
 }

mobile/src/main/java/com/birdsounds/identify/MessageConstants.kt

@@ -0,0 +1,8 @@
+package com.birdsounds.identify
+import android.content.Intent
+
+object MessageConstants {
+    const val intentName = "WearableMessageDisplay"
+    const val message = "Message"
+    const val path = "audio"
+}

mobile/src/main/java/com/birdsounds/identify/MessageListenerService.kt

@@ -0,0 +1,20 @@
+package com.birdsounds.identify
+
+
+import android.content.Intent
+import android.util.Log
+import androidx.localbroadcastmanager.content.LocalBroadcastManager
+import com.google.android.gms.wearable.MessageEvent
+import com.google.android.gms.wearable.WearableListenerService
+
+class MessageListenerService : WearableListenerService() {
+    private val tag = "MessageListenerService"
+    override fun onMessageReceived(p0: MessageEvent) {
+        super.onMessageReceived(p0)
+
+//        Log.i(tag ,p0.data.toString(Charsets.UTF_8))
+//        Log.i(tag, p0.data.toString(Charsets.US_ASCII))
+//        broadcastMessage(p0)
+    }
+
+}

mobile/src/main/java/com/birdsounds/identify/SoundClassifier.kt

@@ -0,0 +1,388 @@
+package com.birdsounds.identify
+
+import android.content.Context
+import android.location.Location
+import android.os.SystemClock
+import android.preference.PreferenceManager
+import android.util.Log
+import org.tensorflow.lite.Interpreter
+import java.io.BufferedReader
+import java.io.File
+import java.io.IOException
+import java.io.InputStreamReader
+import java.nio.ByteBuffer
+import java.nio.FloatBuffer
+import java.nio.channels.FileChannel
+import java.nio.file.StandardOpenOption
+import java.time.LocalDate
+import java.util.Locale
+import java.util.TimerTask
+import java.util.Timer
+import kotlin.concurrent.scheduleAtFixedRate
+import kotlin.math.ceil
+import kotlin.math.cos
+import uk.me.berndporr.iirj.Butterworth
+import kotlin.math.round
+import kotlin.math.sin
+
+class SoundClassifier(
+    context: Context,
+    private val options: Options = Options()
+) {
+    internal var mContext: Context
+    val TAG = "Sound Classifier"
+    init {
+        this.mContext = context.applicationContext
+    }
+
+    class Options(
+        /** Path of the converted model label file, relative to the assets/ directory.  */
+        val labelsBase: String = "labels",
+        /** Path of the converted .tflite file, relative to the assets/ directory.  */
+        val assetFile: String = "assets.txt",
+        /** Path of the converted .tflite file, relative to the assets/ directory.  */
+        val modelPath: String = "model.tflite",
+        /** Path of the meta model .tflite file, relative to the assets/ directory.  */
+        val metaModelPath: String = "metaModel.tflite",
+        /** The required audio sample rate in Hz.  */
+        val sampleRate: Int = 48000,
+        /** Multiplier for audio samples  */
+        val warmupRuns: Int = 3,
+        /** Probability value above which a class in the meta model is labeled as active (i.e., detected) the display. (default 0.01) */
+        var metaProbabilityThreshold1: Float = 0.01f,  //min must be > 0
+        var metaProbabilityThreshold2: Float = 0.008f,  //min must be > 0
+        var metaProbabilityThreshold3: Float = 0.001f,  //min must be > 0
+        /** Probability value above which a class is shown as image. (default 0.5) */
+        var displayImageThreshold: Float = 0.65f,  //min must be > 0
+    )
+
+
+    /** Names of the model's output classes.  */
+    lateinit var labelList: List<String>
+
+    /** Names of the model's output classes.  */
+    lateinit var assetList: List<String>
+
+    /** How many milliseconds between consecutive model inference calls.  */
+    private var inferenceInterval = 800L
+
+    /** The TFLite interpreter instance.  */
+    private lateinit var interpreter: Interpreter
+    private lateinit var meta_interpreter: Interpreter
+
+    /** Audio length (in # of PCM samples) required by the TFLite model.  */
+    private var modelInputLength = 0
+
+    /** input Length of the meta model  */
+    private var metaModelInputLength = 0
+
+    /** Number of output classes of the TFLite model.  */
+    private var modelNumClasses = 0
+    private var metaModelNumClasses = 0
+
+
+    /** Used to hold the real-time probabilities predicted by the model for the output classes.  */
+    private lateinit var predictionProbs: FloatArray
+    private lateinit var metaPredictionProbs: FloatArray
+
+    /** Latest prediction latency in milliseconds.  */
+    private var latestPredictionLatencyMs = 0f
+
+    private var recognitionTask: TimerTask? = null
+
+
+    /** Buffer that holds audio PCM sample that are fed to the TFLite model for inference.  */
+    private lateinit var inputBuffer: FloatBuffer
+    private lateinit var metaInputBuffer: FloatBuffer
+
+    init {
+        loadLabels(context)
+        loadAssetList(context)
+        setupInterpreter(context)
+        setupMetaInterpreter(context)
+        warmUpModel()
+    }
+
+
+    /** Retrieve asset list from "asset_list" file */
+    private fun loadAssetList(context: Context) {
+
+        try {
+            val reader =
+                BufferedReader(InputStreamReader(context.assets.open(options.assetFile)))
+            val wordList = mutableListOf<String>()
+            reader.useLines { lines ->
+                lines.forEach {
+                    wordList.add(it.trim())
+                }
+            }
+            assetList = wordList.map { it }
+        } catch (e: IOException) {
+            Log.e(TAG, "Failed to read labels ${options.assetFile}: ${e.message}")
+        }
+    }
+
+    /** Retrieve labels from "labels.txt" file */
+    private fun loadLabels(context: Context) {
+        val localeList = context.resources.configuration.locales
+        val language = localeList.get(0).language
+        var filename = options.labelsBase + "_${language}.txt"
+
+        //Check if file exists
+        val assetManager = context.assets // Replace 'assets' with actual AssetManager instance
+        try {
+            val mapList = assetManager.list("")?.toMutableList()
+
+            if (mapList != null) {
+                if (!mapList.contains(filename)) {
+                    filename = options.labelsBase + "_en.txt"
+                }
+            }
+        } catch (ex: IOException) {
+            ex.printStackTrace()
+            filename = options.labelsBase + "_en.txt"
+        }
+
+        Log.i(TAG, filename)
+        try {
+            val reader =
+                BufferedReader(InputStreamReader(context.assets.open(filename)))
+            val wordList = mutableListOf<String>()
+            reader.useLines { lines ->
+                lines.forEach {
+                    wordList.add(it)
+                }
+            }
+            labelList = wordList.map { it.toTitleCase() }
+            Log.i(TAG, "Label list entries: ${labelList.size}")
+        } catch (e: IOException) {
+            Log.e(TAG, "Failed to read labels ${filename}: ${e.message}")
+        }
+    }
+
+
+    private fun setupInterpreter(context: Context) {
+        try {
+            val modelFilePath = context.getDir("filesdir", Context.MODE_PRIVATE).absolutePath + "/"+ options.modelPath
+            Log.i(TAG, "Trying to create TFLite buffer from $modelFilePath")
+            val modelFile = File(modelFilePath)
+            val tfliteBuffer: ByteBuffer = FileChannel.open(modelFile.toPath(), StandardOpenOption.READ).use { channel ->
+                channel.map(FileChannel.MapMode.READ_ONLY, 0, channel.size())
+            }
+            Log.i(TAG, "Done creating TFLite buffer from $modelFilePath")
+
+            interpreter = Interpreter(tfliteBuffer, Interpreter.Options())
+        } catch (e: IOException) {
+            Log.e(TAG, "Failed to load TFLite model - ${e.message}")
+            return
+        }
+        // Inspect input and output specs.
+        val inputShape = interpreter.getInputTensor(0).shape()
+        Log.i(TAG, "TFLite model input shape: ${inputShape.contentToString()}")
+        modelInputLength = inputShape[1]
+
+        val outputShape = interpreter.getOutputTensor(0).shape()
+        Log.i(TAG, "TFLite output shape: ${outputShape.contentToString()}")
+        modelNumClasses = outputShape[1]
+        if (modelNumClasses != labelList.size) {
+            Log.e(
+                TAG,
+                "Mismatch between metadata number of classes (${labelList.size})" +
+                        " and model output length ($modelNumClasses)"
+            )
+        }
+        // Fill the array with NaNs initially.
+        predictionProbs = FloatArray(modelNumClasses) { Float.NaN }
+
+        inputBuffer = FloatBuffer.allocate(modelInputLength)
+
+    }
+
+    private fun setupMetaInterpreter(context: Context) {
+
+        try {
+            val metaModelFilePath = context.getDir("filesdir", Context.MODE_PRIVATE).absolutePath + "/"+ options.metaModelPath
+            Log.i(TAG, "Trying to create TFLite buffer from $metaModelFilePath")
+            val metaModelFile = File(metaModelFilePath)
+            val tfliteBuffer: ByteBuffer = FileChannel.open(metaModelFile.toPath(), StandardOpenOption.READ).use { channel ->
+                channel.map(FileChannel.MapMode.READ_ONLY, 0, channel.size())
+            }
+            Log.i(TAG, "Done creating TFLite buffer from $metaModelFilePath")
+
+            meta_interpreter = Interpreter(tfliteBuffer, Interpreter.Options())
+        } catch (e: IOException) {
+            Log.e(TAG, "Failed to load TFLite model - ${e.message}")
+            return
+        }
+        // Inspect input and output specs.
+        val metaInputShape = meta_interpreter.getInputTensor(0).shape()
+        Log.i(TAG, "TFLite meta model input shape: ${metaInputShape.contentToString()}")
+        metaModelInputLength = metaInputShape[1]
+
+        val metaOutputShape = meta_interpreter.getOutputTensor(0).shape()
+        Log.i(TAG, "TFLite meta model output shape: ${metaOutputShape.contentToString()}")
+        metaModelNumClasses = metaOutputShape[1]
+        if (metaModelNumClasses != labelList.size) {
+            Log.e(
+                TAG,
+                "Mismatch between metadata number of classes (${labelList.size})" +
+                        " and meta model output length ($metaModelNumClasses)"
+            )
+        }
+        // Fill the array with 1 initially.
+        metaPredictionProbs = FloatArray(metaModelNumClasses) { 1f }
+        metaInputBuffer = FloatBuffer.allocate(metaModelInputLength)
+
+    }
+
+    fun runMetaInterpreter(location: Location) {
+        val dayOfYear = LocalDate.now().dayOfYear
+        val week = ceil( dayOfYear*48.0/366.0) //model year has 48 weeks
+        lat = location.latitude.toFloat()
+        lon = location.longitude.toFloat()
+
+        val weekMeta = cos(Math.toRadians(week * 7.5)) + 1.0
+
+        metaInputBuffer.put(0, lat)
+        metaInputBuffer.put(1, lon)
+        metaInputBuffer.put(2, weekMeta.toFloat())
+        metaInputBuffer.rewind() // Reset position to beginning of buffer
+        val metaOutputBuffer = FloatBuffer.allocate(metaModelNumClasses)
+        metaOutputBuffer.rewind()
+        meta_interpreter.run(metaInputBuffer, metaOutputBuffer)
+        metaOutputBuffer.rewind()
+        metaOutputBuffer.get(metaPredictionProbs) // Copy data to metaPredictionProbs.
+
+
+        for (i in metaPredictionProbs.indices) {
+            metaPredictionProbs[i] =
+                if (metaPredictionProbs[i] >= options.metaProbabilityThreshold1) {
+                    1f
+                } else if (metaPredictionProbs[i] >= options.metaProbabilityThreshold2) {
+                    0.8f
+                } else if (metaPredictionProbs[i] >= options.metaProbabilityThreshold3) {
+                    0.5f
+                } else {
+                    0f
+                }
+        }
+    }
+
+    private fun warmUpModel() {
+        generateDummyAudioInput(inputBuffer)
+        for (n in 0 until options.warmupRuns) {
+
+            // Create input and output buffers.
+            val outputBuffer = FloatBuffer.allocate(modelNumClasses)
+            inputBuffer.rewind()
+            outputBuffer.rewind()
+            interpreter.run(inputBuffer, outputBuffer)
+
+        }
+    }
+    private fun generateDummyAudioInput(inputBuffer: FloatBuffer) {
+        val twoPiTimesFreq = 2 * Math.PI.toFloat() * 1000f
+        for (i in 0 until modelInputLength) {
+            val x = i.toFloat() / (modelInputLength - 1)
+            inputBuffer.put(i, sin(twoPiTimesFreq * x.toDouble()).toFloat())
+        }
+    }
+    private fun String.toTitleCase() =
+        splitToSequence("_")
+            .map { it.replaceFirstChar { if (it.isLowerCase()) it.titlecase(Locale.ROOT) else it.toString() } }
+            .joinToString("_")
+            .trim()
+
+    companion object {
+        private const val TAG = "SoundClassifier"
+        var lat: Float = 0.0f
+        var lon: Float = 0.0f
+        /** Number of nanoseconds in a millisecond  */
+        private const val NANOS_IN_MILLIS = 1_000_000.toDouble()
+    }
+
+    private fun startRecognition() {
+        if (modelInputLength <= 0 || modelNumClasses <= 0) {
+            Log.e(TAG, "Switches: Cannot start recognition because model is unavailable.")
+            return
+        }
+        val sharedPref = PreferenceManager.getDefaultSharedPreferences(mContext)
+        val highPass = sharedPref.getInt("high_pass",0)
+        val butterworth = Butterworth()
+        butterworth.highPass(6, 48000.0, highPass.toDouble())
+
+        val circularBuffer = ShortArray(modelInputLength)
+
+        var j = 0 // Indices for the circular buffer next write
+
+        Log.w(TAG, "recognitionPeriod:"+inferenceInterval)
+        recognitionTask = Timer().scheduleAtFixedRate(inferenceInterval, inferenceInterval) task@{
+            val outputBuffer = FloatBuffer.allocate(modelNumClasses)
+            val recordingBuffer = ShortArray(modelInputLength)
+
+            // Load new audio samples
+//            val sampleCounts = loadAudio(recordingBuffer)
+            val sampleCounts = 0;
+            if (sampleCounts == 0) {
+                return@task
+            }
+
+
+            // Copy new data into the circular buffer
+            for (i in 0 until sampleCounts) {
+                circularBuffer[j] = recordingBuffer[i]
+                j = (j + 1) % circularBuffer.size
+            }
+
+            // Feed data to the input buffer.
+            var samplesAreAllZero = true
+            for (i in 0 until modelInputLength) {
+                val s = circularBuffer[(i + j) % modelInputLength]
+                if (samplesAreAllZero && s.toInt() != 0) {
+                    samplesAreAllZero = false
+                }
+                if (highPass==0)  inputBuffer.put(i, s.toFloat())
+                else inputBuffer.put(i, butterworth.filter(s.toDouble()).toFloat())
+            }
+
+            if (samplesAreAllZero) {
+                Log.w(TAG, mContext.resources.getString(R.string.samples_zero))
+
+
+                return@task
+            }
+
+            val t0 = SystemClock.elapsedRealtimeNanos()
+            inputBuffer.rewind()
+            outputBuffer.rewind()
+            interpreter.run(inputBuffer, outputBuffer)
+            outputBuffer.rewind()
+            outputBuffer.get(predictionProbs) // Copy data to predictionProbs.
+
+            val probList = mutableListOf<Float>()
+//            if (mBinding.checkIgnoreMeta.isChecked){
+//                for (value in predictionProbs) {
+//                    probList.add(1 / (1 + kotlin.math.exp(-value)))  //apply sigmoid
+//                }
+//            } else {
+                for (i in predictionProbs.indices) {
+                    probList.add( metaPredictionProbs[i] / (1+kotlin.math.exp(-predictionProbs[i])) )  //apply sigmoid
+                }
+//            }
+
+//            if (mBinding.progressHorizontal.isIndeterminate){  //if start/stop button set to "running"
+//                probList.withIndex().also {
+//                    val max = it.maxByOrNull { entry -> entry.value }
+//                    updateTextView(max, mBinding.text1)
+//                    updateImage(max)
+                    //after finding the maximum probability and its corresponding label (max), we filter out that entry from the list of entries before finding the second highest probability (secondMax)
+//                    val secondMax = it.filterNot { entry -> entry == max }.maxByOrNull { entry -> entry.value }
+//                    updateTextView(secondMax,mBinding.text2)
+//                }
+//            }
+
+            latestPredictionLatencyMs =
+                ((SystemClock.elapsedRealtimeNanos() - t0) / 1e6).toFloat()
+        }
+    }
+}

mobile/src/main/java/com/birdsounds/identify/ui/theme/Color.kt

@@ -0,0 +1,11 @@
+package com.birdsounds.identify.ui.theme
+
+import androidx.compose.ui.graphics.Color
+
+val Purple80 = Color(0xFFD0BCFF)
+val PurpleGrey80 = Color(0xFFCCC2DC)
+val Pink80 = Color(0xFFEFB8C8)
+
+val Purple40 = Color(0xFF6650a4)
+val PurpleGrey40 = Color(0xFF625b71)
+val Pink40 = Color(0xFF7D5260)

mobile/src/main/java/com/birdsounds/identify/ui/theme/Theme.kt

@@ -0,0 +1,57 @@
+package com.birdsounds.identify.ui.theme
+
+import android.app.Activity
+import android.os.Build
+import androidx.compose.foundation.isSystemInDarkTheme
+import androidx.compose.material3.MaterialTheme
+import androidx.compose.material3.darkColorScheme
+import androidx.compose.material3.dynamicDarkColorScheme
+import androidx.compose.material3.dynamicLightColorScheme
+import androidx.compose.material3.lightColorScheme
+import androidx.compose.runtime.Composable
+import androidx.compose.ui.platform.LocalContext
+
+private val DarkColorScheme = darkColorScheme(
+    primary = Purple80,
+    secondary = PurpleGrey80,
+    tertiary = Pink80
+)
+
+private val LightColorScheme = lightColorScheme(
+    primary = Purple40,
+    secondary = PurpleGrey40,
+    tertiary = Pink40
+
+    /* Other default colors to override
+    background = Color(0xFFFFFBFE),
+    surface = Color(0xFFFFFBFE),
+    onPrimary = Color.White,
+    onSecondary = Color.White,
+    onTertiary = Color.White,
+    onBackground = Color(0xFF1C1B1F),
+    onSurface = Color(0xFF1C1B1F),
+    */
+)
+
+@Composable
+fun IdentifyTheme(
+    darkTheme: Boolean = isSystemInDarkTheme(),
+    // Dynamic color is available on Android 12+
+    dynamicColor: Boolean = true,
+    content: @Composable () -> Unit
+) {
+    val colorScheme = when {
+      dynamicColor && Build.VERSION.SDK_INT >= Build.VERSION_CODES.S -> {
+        val context = LocalContext.current
+        if (darkTheme) dynamicDarkColorScheme(context) else dynamicLightColorScheme(context)
+      }
+      darkTheme -> DarkColorScheme
+      else -> LightColorScheme
+    }
+
+    MaterialTheme(
+      colorScheme = colorScheme,
+      typography = Typography,
+      content = content
+    )
+}

mobile/src/main/java/com/birdsounds/identify/ui/theme/Type.kt

@@ -0,0 +1,34 @@
+package com.birdsounds.identify.ui.theme
+
+import androidx.compose.material3.Typography
+import androidx.compose.ui.text.TextStyle
+import androidx.compose.ui.text.font.FontFamily
+import androidx.compose.ui.text.font.FontWeight
+import androidx.compose.ui.unit.sp
+
+// Set of Material typography styles to start with
+val Typography = Typography(
+    bodyLarge = TextStyle(
+        fontFamily = FontFamily.Default,
+        fontWeight = FontWeight.Normal,
+        fontSize = 16.sp,
+        lineHeight = 24.sp,
+        letterSpacing = 0.5.sp
+    )
+    /* Other default text styles to override
+    titleLarge = TextStyle(
+        fontFamily = FontFamily.Default,
+        fontWeight = FontWeight.Normal,
+        fontSize = 22.sp,
+        lineHeight = 28.sp,
+        letterSpacing = 0.sp
+    ),
+    labelSmall = TextStyle(
+        fontFamily = FontFamily.Default,
+        fontWeight = FontWeight.Medium,
+        fontSize = 11.sp,
+        lineHeight = 16.sp,
+        letterSpacing = 0.5.sp
+    )
+    */
+)

mobile/src/main/res/values/strings.xml

@@ -1,3 +1,6 @@
 <resources>
     <string name="app_name">identify</string>
+    <string name="title_activity_download">DownloadActivity</string>
+    <string name="error_download">Download Error</string>
+    <string name="samples_zero">Samples are all zero</string>
 </resources>