Creating a Simple Keyword Spotting Algorithm in Go Programming

Keyword spotting is a popular technique used in speech recognition systems to detect specific words or phrases within an audio signal. With the Go programming language, it's possible to build a keyword spotting system that can recognize keywords in real-time audio streams. In this tutorial, we'll walk you through the process of building a simple keyword spotting system using Go.

Step 1: Install Required Packages

Before we begin, ensure that you have the latest version of Go installed on your machine. Additionally, we'll be using two external packages: PortAudio and GoCV. To install these packages, open your terminal and type the following commands:

• go get -u github.com/gordonklaus/portaudio
• go get -u -d gocv.io/x/gocv

Step 2: Record Audio Input

Next, we need to record audio input from the microphone. We'll use the PortAudio package to accomplish this. Here's a code snippet to record audio input:

func record() []float32 {
    in := make([]float32, 512)
    stream, err := portaudio.OpenDefaultStream(1, 0, 44100, len(in), &in)
    if err != nil {
        panic(err)
    }
    defer stream.Close()
    if err := stream.Start(); err != nil {
        panic(err)
    }
    defer stream.Stop()
    var samples []float32
    for {
        if err := stream.Read(); err != nil {
            panic(err)
        }
        samples = append(samples, in...)
    }
    return samples
}

This function creates a PortAudio stream and reads audio input from the microphone in chunks of 512 samples. It continuously records audio input until the function is stopped.

Step 3: Load Keywords

To detect keywords, we need to define a set of keywords that we want to detect. We'll store the keywords in a slice of strings. Here's an example:

keywords := []string{"hello", "world", "go", "programming"}

Step 4: Process Audio Input

Now that we have recorded audio input and defined our keywords, we need to process the audio input to detect if any of our keywords are present. We'll use the GoCV package to convert the audio input into a spectrogram. Here's an example code snippet to accomplish this:

func process(samples []float32) [][]float64 {
    spec := gocv.NewMat()
    defer spec.Close()
    gocv.DFT(gocv.NewMatFromFloat32(samples), &spec, gocv.DftForward|gocv.DftScale)
    specRows := spec.Rows()
    specCols := spec.Cols()
    specData := spec.DataPtrFloat64()
    specDataSize := len(specData)
    spectrum := make([][]float64, specRows)
    for i := range spectrum {
        spectrum[i] = make([]float64, specCols)
    }
    k := 0
    for i := range spectrum {
        for j := range spectrum[i] {
            spectrum[i][j] = math.Sqrt(math.Pow(specData[k], 2)+math.Pow(specData[k+1], 2)) / float64(specDataSize/2)
            k += 2
        }
    }
    return spectrum
}

This function converts the audio input into a spectrogram by performing a Fourier transform on the audio signal. It then normalizes the spectrogram and returns it as a two-dimensional slice of floats.

Step 5: Detect Keywords

Now that we have our spectrogram, we need to search for our keywords within it. We'll do this by calculating the dot product between each keyword's spectrogram and the current audio input spectrogram. If the dot product is above a certain threshold, we'll consider the keyword detected. Here's an example code snippet to accomplish this:

func detectKeywords(spectrum [][]float64, keywords []string, threshold float64) string {
    for _, keyword := range keywords {
        keywordSpectrum := getKeywordSpectrum(keyword)
        dotProduct := calcDotProduct(spectrum, keywordSpectrum)
        if dotProduct > threshold {
            return keyword
        }
    }
    return ""
}

func getKeywordSpectrum(keyword string) [][]float64 {
    // Convert keyword to spectrogram
    // code to convert keyword to spectrogram
}

func calcDotProduct(spectrum1 [][]float64, spectrum2 [][]float64) float64 {
    // Calculate dot product between two spectrograms
    // code to calculate dot product
}

This function takes in the spectrogram and the keywords, and searches for a match by calculating the dot product between each keyword's spectrogram and the current audio input spectrogram. If the dot product is above a certain threshold, we consider the keyword detected and return it.

Step 6: Putting It All Together

Finally, we need to put all the pieces together. Here's an example code snippet that shows how to use all the functions we've defined:

func main() {
    keywords := []string{"hello", "world", "go", "programming"}
    threshold := 1000.0
    for {
        samples := record()
        spectrum := process(samples)
        keyword := detectKeywords(spectrum, keywords, threshold)
        if keyword != "" {
            fmt.Println("Detected keyword:", keyword)
        }
    }
}

This code continuously records audio input, converts it to a spectrogram, and detects any keywords present in the audio input. If a keyword is detected, the program prints a message indicating which keyword was detected.

Conclusion

In this tutorial, we've shown how to build a simple keyword spotting system using the Go programming language. We used the PortAudio package to record audio input, the GoCV package to convert the audio input into a spectrogram, and implemented a simple algorithm to detect keywords in the spectrogram. This is just the tip of the iceberg, and there are many ways to improve this system's performance. However, this tutorial should give you a good starting point to build your own keyword spotting system using Go.