Sopare precision and accuracy

Whoa, it’s about time to talk about accuracy and precision in terms of SOPARE. SOPARE is a Python project that listens to microphone input and makes predictions from trained sounds like spoken words. Offline and in real time.

Before we go into the details we make a quick excursion how SOPARE is processing sound. The microphone listens permanent and records every sound in small chunks. As soon as the volume of a sound reaches a specified threshold, SOPARE adds some small chunks and creates a bigger chunk. At this time, SOPARE has an array of data in raw mic input format. The input receives some filtering (HANNING) and the time domain data is transformed into the frequency domain. Now SOPARE removes unused frequencies as specified in the configuration (LOW_FREQ and HIGH_FREQ).

At this stage SOPARE is able to compress the data (MIN_PROGRESSIVE_STEP, MAX_PROGRESSIVE_STEP). Compression is a big factor of precision. Progressive steps mean that a number of frequencies are combined into one value. A progressive step of 100 takes 100 values and creates one (1) combined value. This is a very rough preparation and a good way to create lots of false positives. The opposite would be a step of one (1) which would use each frequency for the characteristic and prediction and represents the max. accuracy – but maybe also the worst true positive recognition.

This is how the process looks like. From the full blown time domain data (40000), to the specified number of frequencies (600) and at the end there is a compressed set of data (24) which is quite clear and used for the predictions.

 

 

You need to test around and find some good values for your setup and environment. If you have optimal values, train your sound patterns.

Please note that the values in the section „Stream prep and silence configuration options“ must be used for training and whenever you change them you need to do a new training round. This means remove the trained files via

mv dict/*.raw /backup

or

rm dict/*.raw

and train again!

Now let’s talk about options to enhance precision and accuracy. First of all, you should note that one identifier is always susceptible for false positives. Checking for two or more patterns/words increase the precision big time.

The second option is to make use of the config options to increase the accuracy. Let’s start with the one that identifies a word or pattern:

MARGINAL_VALUE

The marginal value can have a range between 0 and 1. Zero (0) means that everything will be identified as the beginning of a word, 1 means that the trained sample and the current sound must match 100%. Good values lie between between 0.7 and 0.9. Test around how high you can increase the value while still getting real results. For testing purpose keep this value quite low.

MIN_CROSS_SIMILARITY

is the option that is used for comparison. Again, 0 means everything is a match and 1 means that the trained pattern and the current sound must match 100%. For one word scenarios, this value can be quite high, two or more words require normally lower values as the transitions from two patterns are most likely not as single trained words. Good values in my setups are between 0.6 and 0.9. 0.9 for single words, lower values for multiple word recognition.

The following values have a huge impact but I can’t hand out best case values. Instead, they require some manual testing and adjustment:

MIN_LEFT_DISTANCE
MIN_RIGHT_DISTANCE

These values are somehow special. For each word/pattern SOPARE calculates the distance from the trained word and the current sound. A low distance means that the characteristic is similar, high distances means that there is a difference. Left and right means that the frequency ranges are halved and the lower and higher bandwidth is compared respectively. Even if a prediction for the whole word is very close, even a small distance can be essential to filter out false positives. The debug option reveals the most important values:

sorted_best_match: [[MIN_CROSS_SIMILARITY, MIN_LEFT_DISTANCE, MIN_RIGHT_DISTANCE, START_POS, LENGTH, u'PREDICTION'], [MIN_CROSS_SIMILARITY, MIN_LEFT_DISTANCE, MIN_RIGHT_DISTANCE, START_POS, LENGTH, u'PREDICTION']]

Again, this requires some fiddling around to find the optimal values that gives true positive and avoid the false ones…start with high values and reduce until you are satisfied. In my smart home light control setup the values are around 0.3 and my false positive rate is near zero although SOPARE is running 24/7 and my house is quite noisy (kids, wife, …).

The last config options to consider is the calculation basis for the value „MIN_CROSS_SIMILARITY“. The sum of the three following values should be 1:

SIMILARITY_NORM
SIMILARITY_HEIGHT
SIMILARITY_DOMINANT_FREQUENCY

„SIMILARITY_NORM“ is the comparison of the FFT similarity.

„SIMILARITY_HEIGHT“ compares against the time domain shape. Good if you want to consider a certain volume.

„SIMILARITY_DOMINANT_FREQUENCY“ is the similarity factor for the dominant frequency (f0).

I recommend to play around with this values and learn the impacts. Based on the environment, sound and the desired outcome there are plenty of possible combinations. Here are some examples:

Puuhhh, this post got longer than expected. My next task is to create some visuals for a better understanding. In the meantime, please give me some feedback.

I had to split the content into several parts.

Part 1:

Part 2:

I’ll add more parts when they are ready … please stay tuned ūüėČ

Sopare basic usage. Voice controlling a magic mirror

In the last post I did a quick sopare intro and we controlled a robotic arm via voice. Today I want to focus on simple one word commands and how to add custom features to sopare. And because I need something to control I use a smart mirror web interface which is one of my next projects I’m working on. It’s not yet a mirror but the frame, screen, a usb mic and some more parts are already assembled and I think this is a perfect example how to use sopare with one word commands.

The magic mirror prototype that will be controlled via voice. Offline and in real time.

The magic mirror prototype that will be controlled via voice

So, let’s start with the requirements. Obviously, you need a Raspberry Pi 2/3. And a microphone. All my sopare systems are using USB microphones. Here is a list that I’m using for different¬†objectives:

  • Blue Microphones Snowball USB Mic (light control)
  • Samson Meteor Mic USB Studio (robotic arm control)
  • Foxnovo Portable USB 2.0 Mic (magic mirror control)

And of course, you need sopare in the latest version. Before we start the training, it’s a good time to check and adjust the mic input. As I’m working most of the time with a headless Pi, „alsamixer“ is my preferred tool. Just make sure that the input is not too high and not too low. I get good results with mic input levels around the 2/3 mark when the mic input level is not yet in the red sector (see the video for a visual reference). Continue Reading

Raspberry Pi and offline speech recognition

Yes, I must admit the test phase went longer than initially thought. But good things take time, right. When one develops a speech recognition software or a pattern detection system stuff can go horrible wrong and the learning curve potentiates at some point.

But anyway, what the hell am I talking about? In a nutshell about SoPaRe. Or the SOund PAttern REcognition project. With Sopare¬†and a Raspberry Pi (technically it works on any Linux system with a multi core environment) everybody can voice control stuff. Like lights, robotic arms, general purpose input and output…offline and in real time.

Voice, speech, microphone, Raspberry Pi, Sound Pattern Recognition with Sopare, voice control a robotic arm

Even without a wake up word. The local dependencies are minimal. Sopare is developed in Python. The code is on GitHub. Cool? Crazy? Spectacular? Absolutely! I made a video to show you whats possible. In the video I control a robotic arm. With my voice. Running Sopare on a Raspberry Pi. In real time. Offline. And it is easy as eating cake. You are pumped? So am I. Here it is:

I may prepare some more tutorials about fine tuning, increase precision and the difference between single and multiple word detection. Let me know what you are using Sopare for, what’s missing or about potential issues.

Happy voice controlling. Have fun ūüôā

Smart home and voice control – SoPaRe beta testing

More than a year ago I’ve written about voice controlled stuff. Enterprise NCC-1701-D like. As you all know: with the rise of cloud APIs this can be accomplished with some work. Downside is that all your talks will be processed in the cloud which means that you may lose your privacy. As I like the Raspberry Pi my goal was to have something running locally on a Pi. To be more specific: on a Raspberry Pi 2. After trying Jasper and some other projects my personal conclusion was that the small device is not powerful enough for the heavy lifting. This said I just want instant results. And I want to talk from anywhere in the room. I don’t care where the microphone is located. So I started the project¬†SoPaRe to figure out what is possible. My goals were (and still are):

  • Real time audio processing
  • Must run on small credit card sized, ARM powered computers like Raspberry Pi, Banana Pi and alike
  • Pattern/voice recognition for only a few words
  • Must work offline without immanent dependencies to cloud APIs
  • Able¬†to talk free from anywhere in a room

I must admit that I did not expect much trouble as it’s only data processing. Well, I changed my view and learned a lot. My current¬†result is a first usable system¬†that is able to learn sounds (in my case words) and recognize them even when I not talk directly in the microphone but from 2 meter away and from different angles. But let’s start with some basics. The following image shows the printed result of me saying three words: „computer light off“.

computer_licht_aus

In memory we talk about 80000 values that are generated in roughly 3 seconds. As one of my primary goals was real time processing this number is huge. As the Raspberry PI 2 has 4 cores one of my first decisions was to leverage real threads and process the data on different cores to get a good throughput. Another broad idea was to crunch the data and work with just a small characteristic of the sound. This diagram shows the current project architecture:

sopare_architecture

First of all we have to „tokenize“ a sound into small parts that can be compared. Like single words from a sentence:

computer_licht_aus_word_tokenizer

In the current version even a single word is parted into smaller parts like „com-pu-ter“ and for all this parts a characteristic is generated. These characteristics can be stored for further comparison. I tried quite some stuff but I get decent¬†results with a combination of¬†a condensed¬†fast Fourier¬†transformation and rough meta information like length and peaks.

The current version is able to not only match¬†learned words in a sentence but also does this is a real environment. This means standing in a room and the microphone is somewhere located in the corner. Or speaking from quite a distance. On the other hand I still get false¬†positives as the approach is rough. But I’m quite happy with the current state that’s why I talk about it now. The project (SoPaRe) incl. the source code is located on GitHub. Happy to receive your feedback or comments and of course, if you are using SoPaRe, please tell me about it!

My next step is to kick off the beta testing and enhance here and there. Will write again when we have more results after the test phase ūüôā

Android offline speech recognition

Released today the¬†OXAndroidDemo¬†app on GitHub. As this app uses Android speech recognition features, it’s worth to mention that with Android >= 4.3 the speech recognition no longer requires the Google online service. Simple install the language files.

Goto „Settings“ ‚Üí¬†„My device“ ‚Üí¬†„Language & input“

Make sure to select „Google“ and click the gear. Next step is to select „Offline Speech Recognition“ and download your favorite languages.

Enjoy the faster and more anonymous speech recognition.

Smart home and voice recognition

When I was younger I really liked the computer of the Enterprise in Star Trek TNG. Always on, always listening. „He“¬†understands all languages, accents and background sound is easily filtered out. Even with red alert in the background and explosions all around. Oh, and I still like it. Today I have my own¬†„smart home“ and can control stuff¬†with my voice with an application written for Android. Easy as eating cake, right. Have done this in a few ours in my spare time. But wait. There is something missing. It’s not always on. I can’t just run around and say „Computer, locate [PUT ANY NAME HERE]“ or „Computer, it’s to hot. Please reduce temperature by 2¬į“. You ask why?

Current issues:

– Maturity of voice recognition (I’m talking Linux here, more concrete Raspberry Pi!) and the overall approach (I don’t want to send every word to a Google service!)
– The environment itself. On the Enterprise you could say something in the endmost edge of any floor. This does currently not work for me at all¬†ūüôā
– Security. Don’t want to allow any stranger to control stuff just by saying something at the front door

My goal is to solve the issues as far as I can and build something which is not only cool but also useful. I’ll keep posting my results and approaches here.