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首页 > 解决方案 > 请求有关可视化工具的 Android Kotlin 音频分析的建议

java - 请求有关可视化工具的 Android Kotlin 音频分析的建议

问题描述

这是我试图实现的目标：

我希望能够从手机麦克风录制的音频并将给定频率的电平处理成一个数组，我可以用它来创建一种“条形图”可视化器。我还需要计算播放歌曲的 BPM，以了解更新可视化器的节奏。

我真正想要的只是获得频率阵列和 BPM 计算。如果我能弄清楚如何处理音频，我就可以处理实际的可视化部分。

这几乎是我想要做的：

这几乎是我想要做的：

从我所做的研究来看，一个可能的解决方案似乎是使用 FFT（快速傅立叶变换）。为此，我发现了这个：stackoverflow。但是那个 stackoverflow 线程是用于 Java 的，而我的应用程序目前是用 Kotlin 编写的。我已经读过可以将 Java 导入 Kotlin，但是在我所做的几次尝试中我无法让它工作。因此，也许对此提出一些建议会有所帮助。

无论如何，我最终还是找到了一个为 Kotlin Nier Vizualizer编写的库。它确实有一个类似于我想复制的可视化，但是对于我的生活，我无法弄清楚我会从哪里拉出频率阵列。我尝试在缓冲区被传递到可视化器之前读取它们，但它只是我收到的一个非常长的字符串。我的意思是，我不确定我在期待什么，但我绝对可以使用一些帮助来了解如何将其转换为我可以实际使用的数据格式。

这是我所在位置的一个示例：KeyFrameMaker.kt

    fun makeKeyFrame() {
        val waveFrac = mWaveAnimator.computeCurrentValue()
        val fftFrac = mFftAnimator.computeCurrentValue()
        if (mWaveAnimator.hasValueUpdated) {
            computedWaveData.originMap { idx, _ ->
                (((mDestWaveData[idx].toInt() and 0xFF) - (mPrevWaveData[idx].toInt() and 0xFF)) * waveFrac + (mPrevWaveData[idx].toInt() and 0xFF)).toByte()
            }
            // Create String for computedWaveData ByteArray
            var hexBuffer = ""
            for (b in computedWaveData) {
                hexBuffer += String.format("%02X", b)
            }
            println("WAVE COMPUTED: $hexBuffer")
        }
        if (mFftAnimator.hasValueUpdated) {
            computedFftData.originMap { idx, _ ->
                ((mDestFftData[idx] - mPrevFftData[idx]) * fftFrac + mPrevFftData[idx]).toByte()
            }
            // Create String for computedFftData ByteArray
            var hexBuffer = ""
            for (b in computedFftData) {
                hexBuffer += String.format("%02X", b)
            }
            println("FFT COMPUTED: $hexBuffer")
        }
    }

在这里，我正在打印假定为 computedFftData 的 ByteArray，它打印的值只是非常长的字符串，例如：“ED000F01020A0D01FAFAFF0003F700FE00FFFFFFFFFE0000000000FF00000000000000000FF000000010000000001000000000000000000000000 至少可以说，我不确定如何使用它来实现我的一系列频率级别的目标。

在这一点上，我想我会把它留在那里，看看我是否能得到任何建议。我也做了很多其他的研究，但我不确定添加更多内容是否真的会有所帮助。绝对希望与对此更了解的人交谈，以为我指明正确的方向。

作为参考，我将使用此 Android Visualizer 类和此处的 RECORD_AUDIO 权限：Android Visualizer 参考

编辑 3：终于初始化了可视化器！

所以事实证明，从@Tenfour04 的挖掘来看，他是正确的，一些 android 设备在可视化器初始化方面存在问题。更多信息在这里：Github 线程。

此时，我正在从 Visualizer 和音频记录会话的 FftDataCapture 侦听器中获取数据。在这一点上我可能会弄清楚，但如果有人想为我和其他最终阅读本文的人提供关于最佳实现的建议，那不会有什么坏处！我肯定会继续用我发现的内容更新这篇文章，以便将来的人们至少有一个可以借鉴的例子。

下一步是获取从 FFT 函数生成的幅度和相位数组，并找出将大量值数组降低到大约 12 个左右的最佳方法……@Tenfour04 提到只在给定位置取设定值，我认为这将是一个很好的解决方案。尽管我对频率知之甚少，但整个频谱的分布并不均等-因此，如果我没记错的话，我们需要从频谱的乞求中获取比最终更多的值...可能是错误的不过，将不得不更多地研究它。

我看到的另一个问题是数组并不总是相等的长度。我相信这意味着我需要等待下一次数据捕获并重新捕获缓冲区？但也不确定这是否准确，也许我可以从给定任何大小数组的某些位置提取值？看起来可能 onWavFormDataCapture 的默认声明有一种等待 ByteArray 的大小等于 mWaveBuffer byteArray 的大小的方法，但是 ByteArray 似乎是可变大小的 - 所以这似乎不是一个有效的根据我的理解解决。

无论如何，我只是在吐口水，并会在我得到它时更新更好的信息。

这是应用 FFT 的音频数据片段：

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Phases: 0.0, 2.2694561, -1.7492068, -1.8925469, -0.15702978, 1.2095926, 0.0, 1.4288993, -0.9272952, -0.5880026, -1.1071488, -0.6435011, -0.5404195, 0.4636476, -2.6779451, -2.3561945, -2.2318394, -0.86217004, 1.1071488, 2.158799, -2.6779451, -0.7853982, 1.815775, 2.6779451, -1.929567, 0.32175055, 1.1071488, 1.4711276, 2.1224513, -1.6814536, -1.1071488, -1.1071488, -1.2490457, -1.2490457, -1.1071488, -1.1071488, -1.5707964, -1.1071488, 0.0, 0.0, -2.3561945, -1.3258177, 0.5880026, 2.55359, -2.3561945, -1.1071488, -0.7853982, -1.1071488, -1.5707964, -1.5707964, -1.5707964, -0.7853982, 3.1415927, -2.6779451, -1.5707964, -0.7853982, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, 0.0, 3.1415927, -2.3561945, -1.5707964, -1.5707964, -1.5707964, -2.3561945, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -2.3561945, -2.3561945, -1.5707964, -1.5707964, -2.3561945, -1.5707964, -2.3561945, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -2.3561945, -1.5707964, -1.5707964, -1.5707964, 0.0, -1.5707964, -2.3561945, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, 0.0, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, 0.0, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, 0.0, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, 0.0, -1.5707964, 0.0, -1.5707964, -1.5707964, -1.5707964, 0.0, 0.0, 0.0, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, 0.0, -1.5707964, 0.0, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, 0.0, -1.5707964, -1.5707964, 0.0, 0.0, 0.0, 0.0, -1.5707964, 0.0, -1.5707964, -1.5707964, -1.5707964, -1.5707964, 0.0, -1.5707964, 0.0, 0.0, -1.5707964, 0.0
Phases: 0.0, 1.2170932, -2.5222197, 1.1243883, 0.094951704, 0.21406068, 1.784857, -2.4329665, -0.4048918, -0.5880026, -0.98279375, -1.1071488, 1.1071488, 0.0, 1.2490457, 2.0899425, 2.7430701, 0.7853982, -1.815775, -0.87605804, 1.5707964, -1.1071488, -2.819842, -0.7853982, 1.0040671, 2.2950463, 2.6363025, 2.7233684, -1.1284221, -1.0516502, -1.3734008, -1.19029, -0.98279375, -1.2490457, -1.2490457, -1.2490457, -1.2490457, -1.2490457, -1.1071488, -1.1071488, -1.1071488, -0.7853982, 0.4636476, -1.5707964, -1.5707964, -1.5707964, -1.2490457, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -0.7853982, -0.7853982, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -2.0344439, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -2.3561945, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -2.3561945, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, 0.0, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, 0.0, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, 0.0, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, 0.0, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, 0.0, -1.5707964, 0.0, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, 0.0, -1.5707964, -1.5707964, -1.5707964, -1.5707964, 0.0, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, 0.0, -1.5707964, -1.5707964, 0.0, 0.0, -1.5707964, 0.0, -1.5707964, -1.5707964, -1.5707964, 0.0, 0.0, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, -1.5707964, 0.0, 0.0, -1.5707964, -1.5707964, 0.0, 0.0, 0.0

这是我现在正在使用的代码：

class MainActivity : AppCompatActivity() {

    val SAMPLING_RATE = 44100
    val REQUEST_CODE_AUDIO_PERMISSION = 1

    private var visualizer: Visualizer? = null

    lateinit var magnitudesArray: FloatArray

    private var mWaveBuffer: ByteArray? = null
    private var mFftBuffer: ByteArray? = null
    private var mDataCaptureSize: Int = 0

    private var mAudioBufferSize: Int = 0
    private var mAudioRecord: AudioRecord? = null

    private var mAudioRecordState: Boolean = false

    @TargetApi(Build.VERSION_CODES.KITKAT)
    @RequiresApi(Build.VERSION_CODES.JELLY_BEAN)
    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)
        setContentView(R.layout.activity_main)

        ensurePermissionAllowed()
        println("OUTSIDE PERMISSIONS CHECK")

        mAudioBufferSize = AudioRecord.getMinBufferSize(SAMPLING_RATE, AudioFormat.CHANNEL_IN_MONO, AudioFormat.ENCODING_PCM_8BIT)
        mAudioRecord = AudioRecord(MediaRecorder.AudioSource.MIC, SAMPLING_RATE, AudioFormat.CHANNEL_IN_MONO, AudioFormat.ENCODING_PCM_8BIT, mAudioBufferSize)

        println("mAudioBufferSize: $mAudioBufferSize")

        if (mAudioRecord!!.state != AudioRecord.STATE_INITIALIZED) println("AudioRecord init failed")
        else                                                     println("AudioRecord init success")

        try {
            println("BEGIN INITIALIZING VIZUALIZER.")
            println("Audio Session ID: ${mAudioRecord!!.audioSessionId}")
            //visualizer = Visualizer(mAudioRecord.audioSessionId).apply {
            visualizer = Visualizer(0).apply {
                enabled = false
                captureSize = 512
                //captureSize = captureSizeRange[1]

                try {
                    scalingMode = Visualizer.SCALING_MODE_NORMALIZED
                } catch (e: NoSuchMethodError) {
                    println("CANT SET SCALING MODE.")
                }
                measurementMode = Visualizer.MEASUREMENT_MODE_NONE

                setDataCaptureListener(object : Visualizer.OnDataCaptureListener {
                    override fun onFftDataCapture(visualizer: Visualizer?, fft: ByteArray?, samplingRate: Int) {
                        val n = fft?.size
                        val magnitudes = FloatArray(n!! / 2 + 1)
                        val phases = FloatArray(n / 2 + 1)
                        magnitudes[0] = Math.abs(fft[0].toFloat())
                        magnitudes[n / 2] = Math.abs(fft[1].toFloat())
                        phases[n / 2] = 0f
                        phases[0] = phases[n / 2]
                        for (k in 1 until n / 2) {
                            val i = k * 2
                            magnitudes[k] = Math.hypot(fft[i].toDouble(), fft[i + 1].toDouble()).toFloat()
                            phases[k] = Math.atan2(fft[i + 1].toDouble(), fft[i].toDouble()).toFloat()
                        }
                        println(magnitudes.joinToString(", "))
                        println(phases.joinToString(", "))
                    }
                    override fun onWaveFormDataCapture(visualizer: Visualizer?, waveform: ByteArray?, samplingRate: Int) {
                        val waveBuffer = mWaveBuffer ?: return
                        if (waveform == null || waveform.size != waveBuffer.size) {
                            return
                        }
                        System.arraycopy(waveform, 0, waveBuffer, 0, waveform.size)
                    }

                }, Visualizer.getMaxCaptureRate(), true, true)
            }.apply {
                mDataCaptureSize = captureSize.apply {
                    mWaveBuffer = ByteArray(this)
                    mFftBuffer = ByteArray(this)
                }
            }
        } catch (e: RuntimeException) {
            println("ERROR DURING VISUALIZER INITIALIZATION: $e")
        }

        record_button.setOnClickListener {
            if (!mAudioRecordState) {
                mAudioRecord!!.startRecording()
                visualizer?.enabled = true

                mAudioRecordState = true
            }
            else {
                mAudioRecord!!.stop()
                visualizer?.enabled = false

                mAudioRecordState = false
            }
        }
    }

    private fun ensurePermissionAllowed() {
        if (ContextCompat.checkSelfPermission(this, android.Manifest.permission.RECORD_AUDIO) != PackageManager.PERMISSION_GRANTED) {
            println("PERMISSION TO RECORD AUDIO DENIED.  REQUESTING.")
            ActivityCompat.requestPermissions(this, arrayOf(android.Manifest.permission.RECORD_AUDIO), REQUEST_CODE_AUDIO_PERMISSION)
        }
        else {
            println("PERMISSION TO RECORD AUDIO GRANTED.")
        }
    }

    override fun onRequestPermissionsResult(requestCode: Int, permissions: Array<out String>, grantResults: IntArray) {
        when (requestCode) {
            REQUEST_CODE_AUDIO_PERMISSION -> {
                if (grantResults[0] != PackageManager.PERMISSION_GRANTED) {
                    Toast.makeText(this, "Demo need record permission, please allow it to show this visualize effect!", Toast.LENGTH_LONG).show()
                    finish()
                }
            }
        }
    }

标签： javaandroidkotlin

解决方案

如果您使用的是 Android Visualizer 类，则不需要其他 FFT 库，因为 Visualizer 类提供了一个getFFT()方法，该方法可以返回已应用 FFT 的音频。您只需将其转换为以 dB 为单位的幅度，即可使其在图形中看起来不错。

在尝试实例化 Visualizer 之前，您需要请求麦克风权限，否则它将无法初始化。Visualizer 主要在 C 中实现，并且主要是一个 JNI 包装器，因此如果出现问题会抛出大量 RuntimeExceptions，因此您需要将初始化和设置调用包装在 try/catch 块中。

lateinit var magnitudesArray: FloatArray

//...

val captureSizeRange = Visualizer.getCaptureSizeRange()

try {
    visualizer = Visualizer(0)
    visualizer?.let {
        scalingMode = Visualizer.SCALING_MODE_NORMALIZED
        captureSize = captureSizeRange[1]
        setDataCaptureListener(this, Visualizer.getMaxCaptureRate(), false, true)
        enabled = true
    }
} catch (e: RuntimeException) {
    //..log it
}
magnitudesArray = FloatArray(captureSizeRange[1] / 2 + 1)

然后，您可以使用文档中给出的公式实现数据捕获侦听器的onFftDataCapture功能，以将传入的 FFT 传输到。对这些值应用 log10 函数以获得适合可视化的幅度（人类听觉解释大约 log10 尺度上的相对声能，这就是为什么声音通常以 dB 为单位描述的原因）。magnitudesArray

它们的典型捕获大小为 1024 或 2048，因此您的阵列中将有 1024 个幅度。为了像您的图片一样获得漂亮的频谱条可视化，我发现如果您只是从幅度数组中选择均匀间隔的值而不是尝试平均它们的范围，它似乎看起来最好。

关于您对 Java 代码的评论......它只是将 Java 库添加到您的项目并从 Kotlin 调用它。你并没有真正“导入”它。Kotlin 文档包含所有详细信息。

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