What’s the difference between binaural sounds compared to stereo? Stereo is created using two sensors and allow us to determine the direction of a sound by measuring the time delay of the incoming signal. A sound to the left will reach the left sensor slightly earlier than the right and so on. But with two sensors measuring time delay we can’t differentiate between sounds on opposite sides of the sensor axis or out of the plane of the sensors. And yet with only two ears we are all able to accurately identify the location of sounds behind or above us. So where is the extra information coming from?

As we have developed from infancy, we have learned how our heads and ears modify sounds from different directions and so can gain extra directional information. The ears are not omnidirectional, they have a strong forward sensitivity and sounds from behind are attenuated. In addition, this attenuation is not uniform across all frequencies and we learn to associate this unique filtering of sounds with the direction from which they originate. This means that the binaural experience is to some extent individual and we’re all tuned to our own particular characteristics. Your auditory pathways are connected differently to mine. This paper describes an experiment (using ferrets of all things) which demonstrates that localisation ability is reduced when listening to sounds modified by a head/ear response that is not the listener’s. By recording sounds using microphones embedded in a dummy head or inserted into the ear canals of real humans recordings can be made that can recreate this effect when listened back to using headphones. However it is not always convenient to record sounds in this way and, as in the case of the binaural game project, we want to synthesise this effect.

In order to generate binaural effects we need to capture the way that the head and ears modify sound. This can be done by measuring the impulse response (IR) at the ears to sounds from different positions around the listener. The impulse response is the response of a system to an infinitely short, infinitely loud signal (infinite spectrum). In reality such signals are difficult to generate so alternative sources are used such as starter pistols. Pistols are good for characterising large spaces such as halls, but in the case where we’re measuring human responses we can use something much gentler such as a time stretched pulse. This is a single low power tone swept over the range of frequencies of interest. Post processing can compress this sweep and determine the IR as if it had been generated by a wideband pulse. What you end up with is a collection of responses known as ‘head related impulse responses’ (HRIRs), or ‘head related transfer functions‘ (HRTFs) in the frequency domain. These can be used to create a binaural sound from a mono sample by convolving the HRIR with the sample. There’s a nice interactive demonstration of this very powerful technique at the Joy of Convolution and a longer discussion of binaural recording here.

Lots of work has already been done in this area, some of which has been very generously shared. A group at IRCAM (Institut de Recherche et Coordination Acoustique/Musique) have made available a number of HRIR measurements from different individuals that can be used to create binaural effects. I’ve written a Java application to apply these HRIRs to mono sounds to create binaural effects. I’ve found that percussive sounds, rich in high frequencies, are most convincing. You can read more about it and download it below. As mentioned, the binaural experience is different for each individual so it’s difficult to predict if a particular HRIR set will work for everyone. You may need to try a number of HRIR sets from the link above before you find one that works for you.

The audio games are still in the development stage, but I’ll post back when there’s anything I can show you.

Download

binaural-1.3.zip

Run

Either double-click the file BinauralSound.jar or from the command line type

java -jar "BinauralSound.jar"

This method is preferable as it’ll let you see any console messages that are generated (usually exceptions due to invalid sound file formats).

Sound test

Check to see if the Java sound system is working correctly by clicking the ‘Sound test’ button. You should hear a 1kHz tone alternating from left to right. On Linux the Java sound system sometimes complains that the host sound device is busy. This can usually be solved by restarting the Linux sound service. For example, if you’re using ALSA then as root type:

/etc/init.d/alsa force-restart

Generating binaural sounds

Download a .zip file of HRTF measurements (strictly speaking HRIR measurements) from the LISTEN HRTF DATABASE and load it into the application (no need to unzip it). Load a short (<1Mb) sample to be transformed and click ‘Play’. The sample must be 44.1kHz PCM and 8, 16 or 24 bit. It can be mono or stereo but if it is the latter then only the left channel will be used to generate the binaural output

Here’s a mono anechoic sample of a snare drum to get you started: SnareDrum.wav

The application will sweep the sample from 0° (dead ahead) around your head anticlockwise in 15° steps. You can also dump the generated binaural sounds as WAV files by clicking ‘Generate files…’.

Don’t forget that you’ll need to listen to the sounds with headphones to experience the effect.

Despite being a very dry and functional book, it manages to capture the tension of the time simply by being so exhaustively comprehensive. Nothing is left to chance. From the more obvious stuff such as the logical grouping of controls in instrument panels and the dimensions of walkways it also covers the minutiae like the optimum resistance of levers and the best actuation force and throw angle of switches. It’s beautifully typeset with clear diagrams and graphs and lots of lovely tables of data. I sometimes worry how excited this kind of thing makes me.

Much of the book details various human parameters, such as height, weight and limb length based on the ‘average male Air Force personnel’. All this data is circa 1955 when, if you are to go by the illustrations, the average Air Force personnel wore slacks and had a short back and sides. However I was very pleased to see I would have been in the 95th percentile regarding height, a veritable man-mountain. But while there’s lots of data that is now obsolete, there’s just as much that is still valid. The chapter on ‘Human Dynamics’ treats the human as another component in the machine with their own unique system response, while the chapter on ‘Selection of Signals’ addresses those tricky situations when you have to choose between a buzzer, a bell or a klaxon. Rather more applicable to today is the section ‘Intelligibility in Speech Communication’ that details methods to maintain speech quality in noisy channels with limited bandwidth. These problems are still with us today, we just have more sophisticated tools to apply to them.

The book is full of simple illustrations from the text – snapshots of ‘the right way’ to do something. They all make perfect sense when you have them pointed out to you and these techniques will be entirely transparent to those using the devices. But omit them, and you can bet they’ll be the first thing an operator notices.

There are so many great articles in this book, and I’m sure I’ll post some more in the future, but currently this image sums it up for me:

Here’s to comfort and efficiency!

Over the next few days I’ll be adding references to the existing content of frisnit.com and then all new content will be added here. There are lots of very exciting new projects in the pipeline and I’m looking forward to sharing them with you.

A popular dating site in the UK lists the ages of the subscribers and the highest and lowest age of the partner that they are interested in. The initial motivation for this project was to discover the optimum age that should be targeted to maximise chances of dating success. However, during the investigation, patterns in the data were observed that could explain the phenomenon of the male mid-life crisis and predict when it is likely to occur for a particular individual. This knowledge could be used to reduce the impact of this difficult period by giving everyone involved adequate time to prepare, or exploited by marketing companies hoping to sell more convertible sports cars.

Data source

This particular dating site was chosen because each user’s record contains their age (a), and the maximum and minimum age of the partner they are looking for (h and l respectively). This age range is referred to as the ‘desired age’ in this discussion. The site is arranged in such a way that this data can be gathered automatically using a Perl script and saved in a tab-delimited text file. From there it can be loaded into most data analysis software packages. Only the three parameters described above were recorded and no personal data was gathered. An example of the data is given in Table 1.

Table 1: Example data

Calculating the desired age range of individuals

500 samples for each age between 20 and 50 were taken of males and females . Towards the extremes of the age range 500 samples were not always available. The average of h and l were taken and the results plotted. These are shown in Figures 1 and 2. The red dots are the average high limit and the blue dots are average low limit for each age. The dots form an almost perfect straight line. The solid red and blue lines are the best fit lines through the data points. These graphs allow an insight into how the ages of partners that males and females are attracted to changes over time.

Fig 1

Fig 2

The graphs can be interpreted as follows. To determine the age range that a person desires, look up their age on the x-axis. Draw a vertical line from here and at the intersections with the low and high limit lines read off the corresponding ages on the y-axis. For example, from Fig 1 the average desired age range for a 30-year old man is between 24 and 32. Working backwards, to find the ages of women who are attracted to 30 year old men, locate 30 on the y-axis of Fig 2 and draw a horizontal line. At the intersections with the low and high limit lines read off the corresponding ages on the x-axis. In this case the range is about 22 to 32. A function to calculate the optimum age range to target when dating is given here.

Fig 3: An outlier

Evaluation of the ‘half your age plus seven’ rule

The ‘half your age plus seven’ rule has long been cited as the minimum age of a woman that a man should date. Fig 4 below shows this rule overlayed on the female desired age chart. As can be seen, this will only be a successful tactic for men aged below about 28, and even then will be far from the optimum value. A more accurate, if less catchy, rule of thumb would be ‘age minus seven, times one point one seven’.

Fig 4

Observations

These graphs reveal several phenomena:

Range of desired age increases with age

The red and blue lines are not parallel and diverge over time for both sexes. While a 25 year old woman will desire a man aged between about 25 and 33 (a range of 8 years), a 45 year old woman will desire a man between about 40 and 52 (a range of 12 years). A similar pattern is seen in the data for males. Basically everyone gets less choosy the older they get.

Desired age range differs between genders

The solid green line shows the path followed by a partner the same age. This makes it easier to visualise the desired age of a potential partner in relation to one’s own age. From this it can be seen that in general, women are attracted to men older than themselves, while men are attracted to younger women. This corresponds well with anecdotal evidence and it is nice to see it confirmed by the data.

Partners may lose attraction over time

The high and low age limits for each sex increase at less than 1 year per year (the gradient of the lines are less than 1). This means that as people age, they will traverse the desired age range of their partner. For a couple who met when they were both within each other’s desired age range, one of the partners will move out of the other’s desired range at some point in the future. For example, let us suppose that a 30 year old man has a 30 year old partner. The partner’s point in the male’s desired age range is now shown by the green line in Fig 1. As time passes, the green line moves closer to the high age limit until it intersects with it when the couple are aged about 46. Beyond this point, while the male is still well within the female’s desired age range (Fig 2) the female is now outside the male’s desired age range suggesting that the male will begin to lose interest and start looking for a younger female. This observation will now be developed in the following section.

Prediction of relationship longevity

The high and low limit lines from each graph can be represented by an equation of the form:

Where m is the gradient of the line and c is its intersection with the y-axis.

To discover the age at which one partner (p) will lose interest in the other partner (q) we must calculate the intersection of the high limit line of partner p and the line traced by partner q’s age. The line traced by partner q’s age has the parameters m=1 and c=Δa, where Δa is the difference in age between p and q (Δa=q-p). Intersection occurs when:

Solving for x gives:

Using the line parameters in Appendix A, the ages at which a particular couple will be attracted to each other can be calculated. This calculation has been implemented in a web page and can be seen here.

Relationship to male mid-life crisis

In the case of females, the point of intersection between their high age limit and the age of a similarly aged partner occurs when the couple are about 146 years old. This seldom occurs except in some Old Testament couples. For males however, this will occur when the couple are about 46 years old. This coincides with the general age of the onset of the male mid-life crisis and may account for some of the behaviour exhibited by men during this period. A mid-life crisis calculator has been developed and can be seen here.

Determining partner compatibility

As discovered above, what you want and what you can get are two different things. Fig 5 shows the male and female desired age ranges plotted as areas on the graph. The intersection of the red and blue areas shows where mutual attraction occurs between the sexes. It is in this area that reproductive effort should be put to maximise the chance of success. Print out this chart and take it to the next party you go to.

Fig 5: Cut out and keep

Appendix A: Line parameters

The parameters for the lines in Figures 1 and 2 are given below:

Table 2