Add FLAC audio quality analysis and spectrum visualization (#110)

Introduces backend support for analyzing FLAC audio files, including technical metrics and frequency spectrum extraction. Adds frontend components and hooks for file selection, analysis, and visualization, integrating a new Audio Quality Analyzer dialog into the UI. Updates types and dependencies to support audio analysis features.

backend/spectrum.go

@@ -0,0 +1,205 @@
+package backend
+
+import (
+	"fmt"
+	"math"
+	"math/cmplx"
+	"os"
+
+	"github.com/mewkiz/flac"
+)
+
+// SpectrumData contains frequency spectrum information
+type SpectrumData struct {
+	TimeSlices []TimeSlice `json:"time_slices"`
+	SampleRate int         `json:"sample_rate"`
+	FreqBins   int         `json:"freq_bins"`
+	Duration   float64     `json:"duration"`
+	MaxFreq    float64     `json:"max_freq"`
+}
+
+// TimeSlice represents spectrum data at a point in time
+type TimeSlice struct {
+	Time       float64   `json:"time"`
+	Magnitudes []float64 `json:"magnitudes"`
+}
+
+// AnalyzeSpectrum decodes FLAC file and performs FFT analysis
+func AnalyzeSpectrum(filepath string) (*SpectrumData, error) {
+	// Open FLAC file
+	stream, err := flac.ParseFile(filepath)
+	if err != nil {
+		return nil, fmt.Errorf("failed to parse FLAC: %w", err)
+	}
+	defer stream.Close()
+
+	info := stream.Info
+	sampleRate := int(info.SampleRate)
+	channels := int(info.NChannels)
+
+	// Read audio samples
+	samples, err := readSamples(stream, channels)
+	if err != nil {
+		return nil, fmt.Errorf("failed to read samples: %w", err)
+	}
+
+	if len(samples) == 0 {
+		return nil, fmt.Errorf("no audio samples found")
+	}
+
+	// Calculate spectrum
+	return calculateSpectrum(samples, sampleRate), nil
+}
+
+// readSamples reads and decodes audio samples from FLAC stream
+func readSamples(stream *flac.Stream, channels int) ([]float64, error) {
+	var allSamples []float64
+	maxSamples := 10 * 1024 * 1024 // Limit to ~10 million samples to avoid memory issues
+
+	// Decode frames
+	for {
+		frame, err := stream.ParseNext()
+		if err != nil {
+			// End of stream
+			break
+		}
+
+		// Convert samples to float64 and mix channels to mono
+		for i := 0; i < frame.Subframes[0].NSamples; i++ {
+			var sample float64
+
+			// Mix all channels to mono by averaging
+			for ch := 0; ch < channels; ch++ {
+				sample += float64(frame.Subframes[ch].Samples[i])
+			}
+			sample /= float64(channels)
+
+			allSamples = append(allSamples, sample)
+
+			// Limit sample count
+			if len(allSamples) >= maxSamples {
+				return allSamples, nil
+			}
+		}
+	}
+
+	return allSamples, nil
+}
+
+// calculateSpectrum performs FFT analysis on audio samples
+func calculateSpectrum(samples []float64, sampleRate int) *SpectrumData {
+	fftSize := 8192
+	numTimeSlices := 300
+
+	duration := float64(len(samples)) / float64(sampleRate)
+
+	samplesPerSlice := len(samples) / numTimeSlices
+	if samplesPerSlice < fftSize {
+		samplesPerSlice = fftSize
+		numTimeSlices = len(samples) / fftSize
+	}
+
+	timeSlices := make([]TimeSlice, 0, numTimeSlices)
+	freqBins := fftSize / 2
+	maxFreq := float64(sampleRate) / 2.0
+
+	for i := 0; i < numTimeSlices; i++ {
+		startIdx := i * samplesPerSlice
+		if startIdx+fftSize > len(samples) {
+			break
+		}
+
+		window := samples[startIdx : startIdx+fftSize]
+
+		windowedSamples := applyHannWindow(window)
+
+		spectrum := fft(windowedSamples)
+
+		magnitudes := make([]float64, freqBins)
+		for j := 0; j < freqBins; j++ {
+			magnitude := cmplx.Abs(spectrum[j])
+
+			if magnitude < 1e-10 {
+				magnitude = 1e-10
+			}
+			magnitudes[j] = 20 * math.Log10(magnitude)
+		}
+
+		timeSlice := TimeSlice{
+			Time:       float64(startIdx) / float64(sampleRate),
+			Magnitudes: magnitudes,
+		}
+		timeSlices = append(timeSlices, timeSlice)
+	}
+
+	return &SpectrumData{
+		TimeSlices: timeSlices,
+		SampleRate: sampleRate,
+		FreqBins:   freqBins,
+		Duration:   duration,
+		MaxFreq:    maxFreq,
+	}
+}
+
+// applyHannWindow applies Hann window to reduce spectral leakage
+func applyHannWindow(samples []float64) []float64 {
+	n := len(samples)
+	windowed := make([]float64, n)
+
+	for i := 0; i < n; i++ {
+		window := 0.5 * (1.0 - math.Cos(2.0*math.Pi*float64(i)/float64(n-1)))
+		windowed[i] = samples[i] * window
+	}
+
+	return windowed
+}
+
+// fft performs Fast Fourier Transform using Cooley-Tukey algorithm
+func fft(samples []float64) []complex128 {
+	n := len(samples)
+
+	x := make([]complex128, n)
+	for i := 0; i < n; i++ {
+		x[i] = complex(samples[i], 0)
+	}
+
+	return fftRecursive(x)
+}
+
+// fftRecursive performs recursive FFT
+func fftRecursive(x []complex128) []complex128 {
+	n := len(x)
+
+	if n <= 1 {
+		return x
+	}
+
+	even := make([]complex128, n/2)
+	odd := make([]complex128, n/2)
+
+	for i := 0; i < n/2; i++ {
+		even[i] = x[2*i]
+		odd[i] = x[2*i+1]
+	}
+
+	evenFFT := fftRecursive(even)
+	oddFFT := fftRecursive(odd)
+
+	result := make([]complex128, n)
+	for k := 0; k < n/2; k++ {
+		t := cmplx.Exp(complex(0, -2*math.Pi*float64(k)/float64(n))) * oddFFT[k]
+		result[k] = evenFFT[k] + t
+		result[k+n/2] = evenFFT[k] - t
+	}
+
+	return result
+}
+
+// GetFileSize helper
+func getSpectrumFileSize(filepath string) (int64, error) {
+	info, err := os.Stat(filepath)
+	if err != nil {
+		return 0, err
+	}
+	return info.Size(), nil
+}