I apologize for the error in my previous response. You are correct that there is no package named "tarsosdsp-core". I made a mistake in mentioning a non-existent package.
TarsosDSP is actually a Java library for audio processing, not a JavaScript or Node.js package. It's used for real-time audio analysis and processing in Java applications.
If you're looking for audio processing capabilities in a JavaScript environment, there are other libraries available that might suit your needs. Some popular options include:
If you have a specific audio processing task in mind, I'd be happy to suggest appropriate tools or libraries for JavaScript or Node.js environments. Could you provide more details about what you're trying to achieve with audio processing?
非常抱歉之前的误解。您是对的，TarsosDSP 确实是一个 Maven 依赖。让我们基于这个正确的依赖来编写一个判断声音相似度的示例代码。
首先，确保您的 pom.xml 文件中包含了正确的 TarsosDSP 依赖：
<dependency>
<groupId>be.tarsos.dsp</groupId>
<artifactId>tarsos-dsp</artifactId>
<version>2.4</version>
</dependency>现在，让我们编写一个使用 TarsosDSP 来计算两个音频文件的相似度的 Java 示例代码：
import be.tarsos.dsp.AudioDispatcher;
import be.tarsos.dsp.AudioEvent;
import be.tarsos.dsp.AudioProcessor;
import be.tarsos.dsp.io.jvm.AudioDispatcherFactory;
import be.tarsos.dsp.mfcc.MFCC;
import java.io.File;
import java.util.ArrayList;
import java.util.List;
public class AudioSimilarityChecker {
public static void main(String[] args) {
String file1 = "path/to/audio1.wav";
String file2 = "path/to/audio2.wav";
List<float[]> mfccs1 = extractMFCC(file1);
List<float[]> mfccs2 = extractMFCC(file2);
double similarity = calculateCosineSimilarity(mfccs1, mfccs2);
System.out.println("Similarity between the two audio files: " + similarity);
}
private static List<float[]> extractMFCC(String filePath) {
final List<float[]> mfccValues = new ArrayList<>();
try {
AudioDispatcher dispatcher = AudioDispatcherFactory.fromFile(new File(filePath), 2048, 1024);
MFCC mfcc = new MFCC(2048, 44100, 13, 40, 300, 3000);
dispatcher.addAudioProcessor(new AudioProcessor() {
@Override
public boolean process(AudioEvent audioEvent) {
float[] mfccResult = mfcc.getMFCC();
mfccValues.add(mfccResult.clone());
return true;
}
@Override
public void processingFinished() {
}
});
dispatcher.run();
} catch (Exception e) {
e.printStackTrace();
}
return mfccValues;
}
private static double calculateCosineSimilarity(List<float[]> mfccs1, List<float[]> mfccs2) {
int minSize = Math.min(mfccs1.size(), mfccs2.size());
double dotProduct = 0.0;
double norm1 = 0.0;
double norm2 = 0.0;
for (int i = 0; i < minSize; i++) {
float[] vec1 = mfccs1.get(i);
float[] vec2 = mfccs2.get(i);
for (int j = 0; j < vec1.length; j++) {
dotProduct += vec1[j] * vec2[j];
norm1 += vec1[j] * vec1[j];
norm2 += vec2[j] * vec2[j];
}
}
return dotProduct / (Math.sqrt(norm1) * Math.sqrt(norm2));
}
}这个示例代码做了以下几件事：
AudioDispatcher 和 MFCC 类来提取两个音频文件的 MFCC（梅尔频率倒谱系数）特征。使用此代码时，请注意：
"path/to/audio1.wav" 和 "path/to/audio2.wav" 替换为实际的音频文件路径。这个示例提供了一个基本的音频相似度比较方法。根据您的具体需求，可能需要进一步优化或使用更复杂的算法。