Monthly Archives: March 2008

MP3s and the Degradation of Listening

Don’t get me wrong! I own three iPods, which I use extensively and absolutely adore for their portability and other obvious advantages. I, of course, use them differently than most listeners. (If you are lazy or impatient, feel free to jump to the bottom of the page and read how.) Most listeners use mp3 players and mp3 files in ways that severely degrade sound quality and eventually deteriorate the listener’s ability to even tell the difference between good and bad sound quality. But more on this a little later.

Disclaimer: For the cynics amongst you, I am not sponsored by any record label trying to boost CD sales; I could actually not care less. All the information below is not product-specific, is based on facts, and is common knowledge to anyone with a basic understanding of the physics of sound, digital sound processing, hearing physiology, and auditory perception. Ignore at your own risk!

CD sound quality

First, let me address some fundamental issues related to the relationship between CD sound data rates and sound quality.

CD quality is usually described in terms of:

  • sampling rate (44,100 samples/sec.),
  • bit rate (16 bits), and
  • stereo presentation.

Doing some simple math, we can figure of that CD-quality sound corresponds to a data rate of 1411 kbits/sec. (44,100 * 16 * 2 = 1,411,200 bits/sec. = ~1411 kbits/sec.) Sampling rate determines the upper frequency limit (corresponding, in general, to timbre, or sound quality) that can be faithfully represented in a digital sound file (about half of the sampling rate). Bit rate determines the dynamic range (i.e. difference between the softest and strongest sound) that can be faithfully represented in a digital sound file (~6 dB per bit).

Given the maximum frequency and dynamic range of safe and functional human hearing (~20 kHz and ~100 dB respectively), CD-quality digital sound is very close to the best sound quality we can ever hear. There have been several valid arguments put forward, advocating the need for sampling rates higher than 44,100 samples/sec. (e.g. 98,200 samples/sec.), bit rates higher than 16 bits (e.g. 24 or 32 bits), and more than two channels (e.g. various versions of surround sound). Depending on the type of sound in question (e.g. the sound’s frequency/dynamic range and spatial spread) and what you want to do with it (e.g. process/analyze it in some way or just listen to it), such increases may or may not result in a perceptible increase in sound quality. So for the vast majority of listening contexts, CD-quality sound (i.e. 1411 kbits/sec. data rate) does correspond to the best quality sound one can hear.

Compressed sound quality

Now, let’s move to compressed quality sound, whether in mp3, iPod, Real, or any other format.

Every sound-compression technique has two objectives:

a) to reduce a sound file’s data rate and therefore overall file size (for easier download and storage) and

b) to accomplish (a) without noticeably degrading the perceived quality of the sound.

Sound-compression algorithms basically remove bits from a digital sound file and select the bits to be removed so that the information that will be lost will not be perceived by listeners as a noticeable loss in quality.

Compression algorithms base their selective removal of information from a digital file on three perceptual principles:

  1. Just noticeable difference in frequency and intensity:
    Our ears’ ability to perceive frequency and intensity differences as pitch and loudness differences respectively is not as fine grained as the frequency and intensity resolution supported by CD-quality sound. So it is possible to selectively remove some relevant information without the listeners noticing their removal.
  2. Masking:
    Strong sounds at one frequency can mask soft sounds at nearby frequencies, making them inaudible. It is, therefore possible to remove digital information representing soft frequencies that are closely surrounded by much stronger frequencies, without the listeners noticing the removal, since they would not have been able to hear such soft sounds in the first place.
  3. Dependence of loudness on frequency:
    Even if different frequencies have the same intensity they do not sound equally loud. In general, for a given intensity, middle frequencies sound louder than high frequencies, which sound louder than low frequencies. Given the phenomenon of masking described above, this dependence of loudness on frequency allows us to remove some soft frequencies even if they are further away from a given strong frequency, providing an additional opportunity to remove bits (information) from a digital file without listeners noticing the loss. In addition, the dynamic range of hearing is much lower for low than for middle and high frequencies and may be adequately represented by ~10 versus 16 bits, offering one more possibility for unnoticeable data-rate reduction.

Different compression algorithms (e.g. mp3, iTunes, etc.) implement the above principles in different ways, and each company claims to have the best algorithm, achieving the most reduction in file size with the least noticeable reduction in sound quality.

Digital music downloads and the stupefaction of a generation of listeners

Regardless of which company and algorithm is the best, one thing is certain. No matter how the previously discussed principles are implemented and no matter how inventive each company’s programmers are, there is no way for the above principles to support the over 90 percent reduction of information required to go from a CD-quality file to a standard mp3. In other words, reducing data rates from CD quality (1411 kbits/sec.) to the standard downloadable-music-file quality (128 kbits/sec.) is impossible without a noticeable deterioration in sound quality.

In fact, the 139th meeting of the Acoustical Society of America devoted an entire session on the matter, with multiple acousticians and music researchers presenting their perceptual studies on the relationship between compression-data rates and sound quality. Based on these and other, more recent, relevant works, it appears that data rates below ~320 kbits/sec. result in clearly noticeable deterioration of perceived sound quality for all sound files with more than minimal frequency, dynamic, and spatial spread ranges. (E.g. listening to early Ramones at low or high data rates will not make as much of a difference as listening to, say, the Beatles’ “Sergeant Pepper” album.) Such low data rates cannot faithfully represent wide ranges of perceivable frequency, intensity, and spatial-separation changes, resulting in ‘mp3s’ that include only a small proportion of the sonic variations included in the originally recorded file.

As data rates drop, there is a gradual deterioration in

a) frequency resolution (loss of high frequencies, translated as loss of clarity),

b) dynamic range (small, dynamic changes become noninterpretable by the compressed file, resulting in flatter ‘volume’ song profiles), and

c) spatial spread (loss of cross-channel differences, resulting in either exaggeration or loss of stereo separation).

When this degradation of sound quality is combined with the fact that most young listeners get their music only online, what we end up with is a generation of listeners that is exposed to, and therefore ‘trained’ in, an impoverished listening environment. Prolonged and consistent exposure to impoverished listening environments is a recipe for cognitive deterioration in listening ability. That is, in the ability to focus attention on and be able to tell the difference between fine (and, if we continue this way, even coarse) sound variations.

Such deterioration will not only affect how we listen to music but also sound perception and communication in general, since our ability to tell the difference between sound sources (i.e. who said what) and sound source locations (i.e. where did the sound come from) is intricately linked to our ability to focus attention on fine sound-quality differences.

What you should do

a) Do not listen to music exclusively in mp3 (or any other compressed) format.
Go to a live concert! Listen to a CD over a good home sound system, set of headphones, or car stereo!

b) Unless a piece of music is not available in another format, do not waste your money on iTunes or any other music download service, until such services start offering data rates greater than 300 kbits/sec.

c) When you load CDs on your iPod or other devise, select the uncompressed conversion rate (e.g. .wav or .aif formats). If you don’t have the hard disk space on your player to do this, convert at the highest available data rate (currently 320kBits/sec on iTunes).

d) Finally, get a good pair of headphones for your mp3 player! The headsets given out with iPods and most mp3 players are of such bad quality that they essentially create a tight bottleneck to the quality of your digital files and players. The response of these headphones has been designed to match the low quality of popular iTunes or other mp3 files (128 kbits/sec). Mp3-player manufacturers do this for two wise (for them) reasons:

i) poor quality headsets are cheap to produce and good enough to reproduce the poor quality mp3s files you are fed, and

ii) poor quality headsets prevent you from creating/requesting music files at higher data rates because when listening over such headphones you cannot even tell the difference between good and bad sound quality.

Well, what can I say? Wake up and listen to the music!

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Viewing Faculty-Development Programs through the Lens of the Technological Pedagogical Content Knowledge (TPCK) Framework

The key word for technology integration in teaching and learning is “integration.” Integration means not to run the elements—technology, teaching strategies, and the subject matter—in isolation. The call for building an integrated model of three domains of knowledge has been made by both researchers and practitioners. In 2006, two scholars from Michigan State University, Punya Mishra and Matt Koehler, put all the pieces together and formulated a conceptual framework of Technological Pedagogical Content Knowledge (TPCK), also known as TPACK (Technology, Pedagogy, and Content Knowledge). Their work was soon acknowledged by the Technology Committee of the American Association of Colleges for Teacher Education (AACTE), who decided to publish a monograph on TPCK and its application on various disciplines of teacher education.

As a member of the technology committee and one of the editors of the book, I consider my term with the AACTE tech committee the most productive period of my life: I not only mothered two children during this time, but also served as a nanny for the committee’s baby: the Handbook of Technological Pedagogical Content Knowledge for Educators.

While nurturing this baby, I felt myself grow with it, just as one can learn a zillion things in a very short time from being a mother. Since mothers do not have time for theory, let me give you a quick bullet-point summary of TPCK:

tpckone.jpg

  1. TPCK(as shown in the graph above) is the intersection of three bodies of knowledge: technology, pedagogy, and content knowledge.
  2. It’s a level of competency at which a teacher will be able to teach the content knowledge (CK) using the right method (P) and with the right technology (T).
  3. There is interaction and interconnection between the three domains (changes in one section will affect the others).
  4. Teaching is a creative process of navigating through the TPCK landscape.
  5. TPCK calls for teacher education to be delivered through a combined model of T, P, and CK, instead of teaching each of them as single subject.

The power of a theory lies in the fact that it provides you with a lens through which you can have a dissected view of a phenomenon, seek reasons behind the facts, and search for better solutions. By plugging TPCK into my daily practice of faculty support and development, I was able to seek reasons behind a few phenomena, such as the following:
“We are overwhelmed!”
– Faculty dissatisfaction with the training program
A typical response we get in a faculty evaluation of a training program is that they are overwhelmed: too much technology, too much information—all to be absorbed in such a short time. (And honestly, they don’t have more time to give you!)

Using the TPCK model to view and analyze knowledge distribution within a faculty-development program, I see that each of the three domains is usually represented by three unique groups: faculty as content knowledge experts, instructional designers as pedagogical specialists, and technologists as the technology gurus.

tpcktwo.jpg

The difference between TPCK for preservice teachers and TPCK for college faculty is that, for faculty, the content knowledge has already been well established, presumably not through a TPCK approach. Therefore, they need to acquire pedagogical and technological knowledge through some make-up programs, such as faculty development in teaching with technology, teaching-excellence seminars, and technology/course-design boot camps.

The other two groups, instructional designers and instructional technologist, on the other hand, have in-depth knowledge in the pedagogy and technology domains.To them, each of the domains—pedagogy and technology—constitutes a discipline by itself (or in some cases, one joint discipline of instructional technology). As Mishra and Koehler pointed out, each discipline has special forms of knowledge that are comprised of knowledge, methods, purpose, and forms of presentations. Like any other discipline, instructional design/instructional technology has its own “rules and regulations” as well as its own disciplinary thinking, which Gardner describes as “mental furniture” or the mold in which people think.

With good will and a strong motivation to help, specialists from the T and P groups have a higher goal of using the development program as an educational process to make the faculty group adopt the disciplinary thinking of their own domain. (A measurement of success at this point would be, “Have you changed your teaching philosophy to become a student-centered instructor?”) To make this happen, one has to bring in the whole discipline, including the knowledge, the methods, the purpose, and the forms of presentations. Now we are talking about knowledge domains, taxonomies, genres of educational philosophies, cognitive process, inventories of teaching styles, inventories of learning styles, and various instructional design models including both the classical and the newly invented ones. Have I missed anything? I’d better not because every construct serves as a base for another, and together, they formed our discipline of instructional technology—or half of it, since the technology part has not been brought in yet. Now imagine squeezing all of these into a few weeks of training (in a condensed format of course—with a reading list for more in-depth exploring). Cognitive overload? It surely will be.

The TPCK framework raised the importance of context and discipline sensitivity as well as the argument of teaching different disciplines differently. Mishra and Koehler cited Donald’s critique of content-neutral, simplistic one-size-fits-all educational strategies. This means faculty-development-program designers have to be extremely sensitive to the faculty’s discipline and tailor their support in a specific and concrete manner. Building a learning community is a great idea. Using blogs and wikis is cool, and collaborative, problem-based learning is a popular concept, but what if a faculty member is just trying to figure out a way to convey some concepts to his first programming class?

Fifteen years ago, a professor in my COBOL class explained the difference between hardware and software as such that “hardware is the male portion of the population that does the work, but it has to be told by the software, the female portion of the population.” It was a bold (and perhaps gender-biased) explanation, but an understanding of the two technical terms of hardware and software was achieved instantaneously and remained in one student’s mind till today. I see this as a good example of TPCK where a faculty member who has in-depth disciplinary knowledge of computer science deployed an effective teaching strategy—a simile to connect a new concept with student’s prior/common knowledge. (I doubt he had ever had a workshop on Schema Theory of Learning.) The technology was a blackboard. And guest what? It worked.

Now I feel like I should stop writing this blog post and get our staff together to redesign our own faculty-development programs. I will share with you more of my findings from viewing things through the lens of TPCK in a few weeks. Here is a heads-up of what I will discuss in my next blog post:

  • Is the course good enough?
    –the different views between a faculty member and an instructional designer
  • What if pedagogical knowledge is my content knowledge?
    –missing a leg in the T-P-CK tripod
  • Paradise
    –the ideal curriculum of a faculty-development program
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Online Learning: Panacea or Curse?

I recently returned from a trip to Thailand, where I was teaching a cohort of graduate students how to use library databases for research. A common question that I was asked upon my return was why an in-person visit was necessary to teach these skills.

I find it interesting that many people believe that technology and in particular synchronous technologies are a panacea that can replace the face-to-face classroom environment. While I believe that these technologies are powerful and can and do extend the reach of traditional classrooms, I also believe it is important to make wise decisions about the use of technology based on the potential audience and their unique needs and attributes.

In this particular situation, where English was not the students’ first language, where their prior experience using libraries was mixed, and where their access to reliable technology was not a given, face-to-face instruction made the most sense.

I believe that instructors who are being asked to take their classes online need to weigh the advantages and disadvantages carefully. What is the motivation for moving to even a hybrid model? Can the students’ needs be met effectively?

I believe that there are certain courses and certain students that should be taught face-to-face in a real classroom. Statistics and math courses are two that come to mind. I suppose there are those that would argue that many people can and do learn these subject without the need to be in a classroom, but I would argue that there are many more students who require the personal interaction that only a live human standing in front of them can provide. This isn’t to say that there aren’t successful online math and statistics courses but more to argue that before you take the entire math department virtual, you take the students’ needs into consideration.

Undergraduates are another population of students that I believe benefit from the interaction of a live instructor standing in front of them. Again, I am sure there are undergraduates who successfully take online classes and have great experiences, but I would argue that this is more the exception than the rule. Most undergraduates that I know are just learning how to balance their responsibilities and adding the responsibility of managing an online learning experience to the mix is a recipe for disaster. I find it laudable that schools often want to find ways to extend their campus to those most vulnerable of dropping out or not even starting, those students for whom time is precious, since they are juggling home, work, and school responsibilities. However, I would argue that too often the time commitment of an online class far outweighs the potential benefit of not having to be in class on a particular day or time. I would also argue that these students are precisely the ones that need the extra attention that a live teacher in a face-to-face class provides. Perhaps the greatest benefit of this extra attention is that it makes students feel like they belong to a community.

Given all of this, you may think that I don’t believe online instruction is a good option, which isn’t true. Instead, I believe that we as instructors and instructional designers need to make good decisions about which classes and which students are part of our online classrooms.

What Can Online Educators Learn from Advertising?

When the final numbers are counted, online advertising is expected to have grown over 25 percent in 2007 to over $21 billion (BusinessWeek). Even a struggling U.S. economy and a looming recession don’t seem capable of stopping the party any time soon (www.clickz.com). One reason online ad spending has grown so rapidly is its ability to provide very detailed analytics. Online campaigns allow advertisers to gather very granular information on who saw an ad, when, how may times, and so on. Ad firms have whole staffs devoted to tracking online campaigns, evaluating data, and determining the effectiveness of the campaign. DoubleClick and Nielson/NetRatings generate millions each year helping to aggregate this data.

Ideally, online classes should be subject to the same level of analysis. Faculty and instructional designers have lots of tools to assist them with the planning and implementation of an online class but very little to assist them in evaluating the class and improving on it for the next quarter or semester. Data is often hard to get at (Have you ever tried to make sense of server logs?) and often there is not enough time and resources to produce a meaningful report that provides any insight.

That’s why I am excited by the Backlot Content Management System by Ooyala. Designed as a video-ad-campaign manager, the system allows you to quickly generate really useful reports that will be extremely valuable to online educators. Backlot lets you see how many times a video was watched, how much of it was watched, how many students watched it, how many times students replayed the video, etc.

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The reporting interface is easy to use, and Backlot includes a download-to-Excel feature should you want to slice and dice your data even more. It beats trying to decipher server logs. I think it can be a huge asset in allowing educators and researchers to truly determine the effectiveness of video in online classrooms.