Cleaned up whitespace

.gitignore

@@ -1,3 +1,3 @@
 .DS_Store
 *.o
-Test_Yin
+Test_Yin

Makefile

@@ -6,16 +6,16 @@ OBJECTS= Test_Yin.o Yin.o
 
 # --- targets
 all:    Test_Yin
-Test_Yin:   $(OBJECTS) 
+Test_Yin:   $(OBJECTS)
 	$(CC) -o Test_Yin  $(OBJECTS) $(LIBS)
-        
+
 Test_Yin.o: Test_Yin.c Yin.h
 	$(CC) $(CFLAGS) -c Test_Yin.c
-       
+
 Yin.o: Yin.c Yin.h
-	$(CC) $(CFLAGS) -c Yin.c 
+	$(CC) $(CFLAGS) -c Yin.c
 
 
 # --- remove binary and executable files
 clean:
-	rm -f Test_Yin $(OBJECTS)
+	rm -f Test_Yin $(OBJECTS)

README.md

@@ -13,4 +13,4 @@ Thanks to [JorenSix](https://github.com/JorenSix/) for the original C++ implemen
 
 The [YIN algorithm](http://audition.ens.fr/adc/pdf/2002_JASA_YIN.pdf) is a popular algorithm for tracking pitch (or the fundamental frequecy) of a monophonic audio signal. This project is an implementation of the Yin algorithm in C, suitable for embedded systems.
 
-The code was ported to C from C++ from the [The Pidato Experiment](https://github.com/JorenSix/). The Pidato project was originally written for the Arduino platform, hence the C++. This is an attempt to port the algorithm implemented in the Pidato project to a more generic module in pure C, and has been used sucessfully in both embedded systems and high level applications.
+The code was ported to C from C++ from the [The Pidato Experiment](https://github.com/JorenSix/). The Pidato project was originally written for the Arduino platform, hence the C++. This is an attempt to port the algorithm implemented in the Pidato project to a more generic module in pure C, and has been used sucessfully in both embedded systems and high level applications.

TestAudio/audioExtract.py

@@ -11,7 +11,7 @@
 audioData = []
 
 # Extract all the samples from the audio file into an array
-for i in range(0,length):    
+for i in range(0,length):
     waveData = waveFile.readframes(1)
     data = struct.unpack("<h", waveData)
     audioData.append(data[0])

Test_Yin.c

@@ -8,9 +8,9 @@
 
 
 /* Audio array with samples from .wav file
- * contains 
- *     #define NUM_SAMPLES 176400 
- *     int audio[176400] = {...} 
+ * contains
+ *     #define NUM_SAMPLES 176400
+ *     int audio[176400] = {...}
  */
 #include "audioData.h"
 
@@ -25,11 +25,11 @@ int main(int argc, char** argv) {
 
 	while (pitch < 10 ) {
 		Yin_init(&yin, buffer_length, 0.05);
-		pitch = Yin_getPitch(&yin, audio);	
+		pitch = Yin_getPitch(&yin, audio);
 		buffer_length++;
 	}
-	
-	
+
+
 	printf("Pitch is found to be %f with buffer length %i and probabiity %f\n",pitch, buffer_length, Yin_getProbability(&yin) );
 	return 0;
-}
+}

Yin.c

@@ -8,7 +8,7 @@
 
 /**
  * Step 1: Calculates the squared difference of the signal with a shifted version of itself.
- * @param buffer Buffer of samples to process. 
+ * @param buffer Buffer of samples to process.
  *
  * This is the Yin algorithms tweak on autocorellation. Read http://audition.ens.fr/adc/pdf/2002_JASA_YIN.pdf
  * for more details on what is in here and why it's done this way.
@@ -22,7 +22,7 @@ void Yin_difference(Yin *yin, int16_t* buffer){
 	for(tau = 0 ; tau < yin->halfBufferSize; tau++){
 
 		/* Take the difference of the signal with a shifted version of itself, then square it.
-		 * (This is the Yin algorithm's tweak on autocorellation) */ 
+		 * (This is the Yin algorithm's tweak on autocorellation) */
 		for(i = 0; i < yin->halfBufferSize; i++){
 			delta = buffer[i] - buffer[i + tau];
 			yin->yinBuffer[tau] += delta * delta;
@@ -35,7 +35,7 @@ void Yin_difference(Yin *yin, int16_t* buffer){
  * Step 2: Calculate the cumulative mean on the normalised difference calculated in step 1
  * @param yin #Yin structure with information about the signal
  *
- * This goes through the Yin autocorellation values and finds out roughly where shift is which 
+ * This goes through the Yin autocorellation values and finds out roughly where shift is which
  * produced the smallest difference
  */
 void Yin_cumulativeMeanNormalizedDifference(Yin *yin){
@@ -58,7 +58,7 @@ void Yin_cumulativeMeanNormalizedDifference(Yin *yin){
 int16_t Yin_absoluteThreshold(Yin *yin){
 	int16_t tau;
 
-	/* Search through the array of cumulative mean values, and look for ones that are over the threshold 
+	/* Search through the array of cumulative mean values, and look for ones that are over the threshold
 	 * The first two positions in yinBuffer are always so start at the third (index 2) */
 	for (tau = 2; tau < yin->halfBufferSize ; tau++) {
 		if (yin->yinBuffer[tau] < yin->threshold) {
@@ -95,26 +95,26 @@ int16_t Yin_absoluteThreshold(Yin *yin){
  * @return             [description]
  *
  * The 'best' shift value for autocorellation is most likely not an interger shift of the signal.
- * As we only autocorellated using integer shifts we should check that there isn't a better fractional 
+ * As we only autocorellated using integer shifts we should check that there isn't a better fractional
  * shift value.
  */
 float Yin_parabolicInterpolation(Yin *yin, int16_t tauEstimate) {
 	float betterTau;
 	int16_t x0;
 	int16_t x2;
-	
+
 	/* Calculate the first polynomial coeffcient based on the current estimate of tau */
 	if (tauEstimate < 1) {
 		x0 = tauEstimate;
-	} 
+	}
 	else {
 		x0 = tauEstimate - 1;
 	}
 
 	/* Calculate the second polynomial coeffcient based on the current estimate of tau */
 	if (tauEstimate + 1 < yin->halfBufferSize) {
 		x2 = tauEstimate + 1;
-	} 
+	}
 	else {
 		x2 = tauEstimate;
 	}
@@ -123,19 +123,19 @@ float Yin_parabolicInterpolation(Yin *yin, int16_t tauEstimate) {
 	if (x0 == tauEstimate) {
 		if (yin->yinBuffer[tauEstimate] <= yin->yinBuffer[x2]) {
 			betterTau = tauEstimate;
-		} 
+		}
 		else {
 			betterTau = x2;
 		}
-	} 
+	}
 	else if (x2 == tauEstimate) {
 		if (yin->yinBuffer[tauEstimate] <= yin->yinBuffer[x0]) {
 			betterTau = tauEstimate;
-		} 
+		}
 		else {
 			betterTau = x0;
 		}
-	} 
+	}
 	else {
 		float s0, s1, s2;
 		s0 = yin->yinBuffer[x0];
@@ -191,32 +191,29 @@ void Yin_init(Yin *yin, int16_t bufferSize, float threshold){
 float Yin_getPitch(Yin *yin, int16_t* buffer){
 	int16_t tauEstimate = -1;
 	float pitchInHertz = -1;
-	
+
 	/* Step 1: Calculates the squared difference of the signal with a shifted version of itself. */
 	Yin_difference(yin, buffer);
-	
+
 	/* Step 2: Calculate the cumulative mean on the normalised difference calculated in step 1 */
 	Yin_cumulativeMeanNormalizedDifference(yin);
-	
+
 	/* Step 3: Search through the normalised cumulative mean array and find values that are over the threshold */
 	tauEstimate = Yin_absoluteThreshold(yin);
-	
+
 	/* Step 5: Interpolate the shift value (tau) to improve the pitch estimate. */
 	if(tauEstimate != -1){
 		pitchInHertz = YIN_SAMPLING_RATE / Yin_parabolicInterpolation(yin, tauEstimate);
 	}
-	
+
 	return pitchInHertz;
 }
 
 /**
- * Certainty of the pitch found 
+ * Certainty of the pitch found
  * @param  yin Yin object that has been run over a buffer
  * @return     Returns the certainty of the note found as a decimal (i.e 0.3 is 30%)
  */
 float Yin_getProbability(Yin *yin){
 	return yin->probability;
 }
-
-
-

Yin.h

@@ -8,7 +8,7 @@
 
 /**
  * @struct  Yin
- * @breif	Object to encapsulate the parameters for the Yin pitch detection algorithm 
+ * @breif	Object to encapsulate the parameters for the Yin pitch detection algorithm
  */
 typedef struct _Yin {
 	int16_t bufferSize;			/**< Size of the audio buffer to be analysed */
@@ -35,12 +35,12 @@ void Yin_init(Yin *yin, int16_t bufferSize, float threshold);
 float Yin_getPitch(Yin *yin, int16_t* buffer);
 
 /**
- * Certainty of the pitch found 
+ * Certainty of the pitch found
  * @param  yin Yin object that has been run over a buffer
  * @return     Returns the certainty of the note found as a decimal (i.e 0.3 is 30%)
  */
 float Yin_getProbability(Yin *yin);
-	
+
 
 
 #endif

audioData.h

@@ -1500,4 +1500,4 @@ int16_t audio[1500] = {
    -25,
    -26,
    -27,
-   -28};
+   -28};