 Why might any DAC sound better using HQPlayer as its front end? Perhaps the largest advantage is the ability to use superior filters to ones found on board any given DAC's CMOS, FPGA or storage logic. Inside a hardware DAC, there is only so much memory and processing power to go around. Perhaps you have noted how difficult it can be to discern the difference in sound when auditioning your DAC with its available choice of filters. It just might be the reason for this is all the onboard filters have compromises built into them for the sake of efficiency. No one is saying these onboard filters are poor or unlistenable. On the contrary, many are outstanding, such as the GTO filter onboard some of the iFi Audio DAC's. But what if you could leverage the power of a modern day PC to do all the filtering work for you? This can become even more of an advantage is you have an compatible NVidia GeForce GPU built in, as the power of the GPU cores can be leveraged using an instruction set called 'CUDA'. To summarize in layman's terms, you may be able to greatly improve the sound of your DAC by bypassing its onboard filters with HQPlayer software. The easiest way to do this is to use a 'Pure DSD' DAC that already bypasses (or should) every DSP from the point of entry to the DAC, with the only action on the 1-bit stream being the final filtration from digital to analog. Another way one might bypass the internal filters is to feed a DAC its maximum PCM rate, which most often overrides any oversampling. With some DACs the rate that bypasses the internal PCM oversampler/filter is 384khz. Others might be 768khz, or even 1.5mhz. HQPlayer is fully capable and quite adept at this too. Using HQPlayer with a DAC in this kind of configuration or mode can elevate even the more modest under $1000, even under $500 DACs to a level on par with many a coveted 'audiophile' piece of hardware costing many, many times more. Of course, one must account for the cost of the PC and software. To have a PC with the necessary power will not be very cheap, neither is the software 'cheap' in the absolute sense. However its value becomes almost immediately notable within the first few days to weeks of use. The typical filters you will find onboard a modern DAC are called things such as Linear Phase, Minimum Phase, Apodizing, Hybrid, NOS, Short, Long. etc. If you open up the manual to HQPlayer 5, which is a MUST for any new owner by the way, you will find a much more detailed list of filters and more filter types. I decided to try and make a small list for us dummies, in simple terms, that might help us understand what these filters are, how they work, and their expected results. This is NOT a comprehensive list of the HQPlayer filters, so if you have a question, please shoot it my way. Ways to find me... here at Euphonic Review (click contact in the above header) our FACEBOOK Euphonic Review page (linked here) Head-fi username is "MLGrado" Audiophile Style you will find me under my actual name "Andrew Allen Ballew" You will most often find me on Facebook. Also at the latter two sites. I frequent them, even if as a lurker, quite often. HQPLAYER FILTERS and DESCRIPTIONSGAUSSIAN FILTERS (imo the best sounding filters in HQPlauer) A Gaussian filter, when used for audio interpolation, essentially acts like a smooth and gentle blender for sounds. Imagine you have a track with some missing parts or gaps, and you want to fill these in so everything sounds continuous and smooth. A Gaussian filter helps by taking the sounds before and after the gap and blending them together in a way that gradually transitions from one to the other, using a bell-shaped curve as its guide. This bell-shaped curve is where the "Gaussian" part of the name comes from, and it's known for its natural and soft blending properties. In the context of audio, using this filter helps in creating a seamless bridge between segments, making the overall track sound cohesive and uninterrupted. This is particularly useful when you need to guess (or interpolate) what the missing audio might have sounded like based on the existing sounds around the gaps. TAKE NOTE: By and large all the filters mentioned are still SINC filters; the 'curve' that is applied is done so within a 'WINDOW', which is a subject for another day. While a gaussian filter theoretically shows NONE of the ringing inherent to the Gibbs phenomenon, It is applied to other filters such as FIR via a 'WINDOW' which means overall the filter does indeed still ring. POLYNOMIAL FILTERS A polynomial filter for audio interpolation is like using a mathematical detective to fill in the missing pieces of a sound puzzle. Imagine you have an audio track, but parts of it are missing or damaged. The polynomial filter looks at the sound just before and just after the missing section and uses mathematical formulas—specifically, polynomial equations—to predict and recreate the missing sounds. The process involves taking known good points of sound and plotting them like dots on a graph. The polynomial filter then draws a smooth, curved line through these points. This curve helps the filter understand the general direction and flow of the sound, allowing it to generate the missing parts in a way that fits naturally with the rest of the audio. SINC FILTERS A sinc filter for audio interpolation can be thought of as a master sculptor who perfectly restores missing parts of a statue. The "sinc" in sinc filter stands for "sine cardinal," a mathematical function that is ideal for reconstructing the exact original signal when some of its parts are missing. Imagine you have a digital audio track, and some pieces of this track are lost or corrupted. The sinc filter works by examining the sound immediately around the gaps. It then uses the sinc function, which acts like a precise mold, to recreate the missing sound segments. This function has a unique shape that helps it to interpolate (or guess) the missing data very accurately, ensuring that the new sounds it adds are in perfect harmony with the surrounding audio. The sinc filter is particularly good at maintaining the original qualities of the sound, just as a skilled sculptor would make sure the new parts of a statue match the style and texture of the original material. The result is a continuous, smooth sound with no noticeable disruptions, making the audio track feel whole and unchanged. Sinc filters are quite standard. Much of the digital audio world is built on the Sinc filter. POLY-SINC FILTERS A poly-sinc filter is a more advanced and refined type of digital filter used in signal processing, particularly for tasks such as interpolation, resampling, or decimation (reducing the sampling rate). It's an extension of the basic sinc filter but with multiple sinc functions combined to achieve better performance and reduce artifacts like ringing and aliasing, which can occur in simpler sinc implementations. The "sinc" function, as used in sinc filters, is ideal for reconstructing signals from sampled data because it perfectly passes through the original sampled points while minimizing distortion between these points. However, a straightforward sinc filter might be limited by practical issues such as requiring a large number of calculations or producing unwanted oscillations. In a poly-sinc filter, multiple sinc functions are used, each tailored to operate over a slightly different range or aspect of the frequency spectrum. This polyphonic approach allows each part of the filter to focus on a specific task, like smoothing out one part of the spectrum more than another, which can result in a higher-quality output with reduced side effects. The combination of multiple sinc responses can be designed to optimize certain desirable characteristics, such as sharp cutoffs in frequency response or minimal phase distortion. FIR FILTERS An FIR filter for audio interpolation can be thought of as a highly skilled tailor who mends holes in a fabric, making sure the new piece blends seamlessly with the old. FIR stands for Finite Impulse Response, which essentially means that this filter uses a specific, limited sequence of numbers (coefficients) to fix or adjust sounds. In the context of audio interpolation, where there are gaps or missing parts in an audio track, the FIR filter works by examining the sounds right before and after the gap. It then uses its set of coefficients as a pattern or template to weave new sound samples that fit perfectly within this gap. Imagine it like a tailor sewing a patch into torn fabric: the FIR filter stitches new sound pieces into the existing audio, ensuring the weave is so fine and the match so perfect that you can't tell where the original sound ends and the new sound begins. It uses the audio surrounding the gap as a guide to reproduce a sound that feels natural and continuous. This makes the entire track play smoothly, as if there were never any interruptions. The FIR filter like the sinc filter is ubiquitous in digital audio. A great majority of DSP functions that can be done on a 1-bit stream such as DSD begin and end with the FIR filter. IIR FILTERS An IIR filter for audio interpolation can be likened to a skilled gardener who prunes a plant to encourage it to grow in a particular shape. IIR stands for Infinite Impulse Response, which means this type of filter can continue responding to a sound input indefinitely, using its past outputs as well as current inputs to determine its outputs. In terms of filling gaps in an audio track, the IIR filter looks at the sound immediately before and possibly even after the missing section. It then uses a formula that takes into account both these sounds and its previous calculations to generate the missing audio. This method is akin to a gardener deciding where to prune or add supports based on both the current shape of a plant and how it has responded to past care. This way, the IIR filter continuously adjusts and recalculates as it fills in the sound, aiming to produce a smooth, natural continuation of the audio. The result is a seamless integration where the new sound blends so well into the existing track that the gaps become imperceptible, much like a well-maintained plant that grows as if it has always been perfectly shaped. CLOSED-FORM FILTERS A closed form filter for audio interpolation is like having a precise recipe for baking a cake where some ingredients are missing from your list. This type of filter looks at the available ingredients (sound segments before and after the missing parts) and uses a precise formula to figure out what should go in the gaps to make the overall cake (or sound track) taste (or sound) complete and natural. The "closed form" aspect means that the filter uses a specific mathematical solution that can directly compute what the missing audio should be, without needing iterative guesses or adjustments. It's like having a mathematical shortcut that tells you exactly how much of each missing ingredient you need to add, based on what you already have. In audio terms, this results in a seamless connection between sound segments, filling in gaps with sound that blends perfectly with the surrounding audio, as if there were never any pieces missing. It’s a neat and efficient way to ensure the final audio track sounds continuous and polished. If I am incorrect, please correct me, but I believe closed-form filters are what Schiit Audio uses in its high-end products. FFT FILTER An FFT filter for audio interpolation works a bit like using a high-tech microscope to fix a piece of a puzzle. FFT stands for Fast Fourier Transform, which is a method used to break down the audio into its basic building blocks, known as frequencies. By analyzing these frequencies, the FFT filter can understand the underlying patterns of the sound. When parts of the audio are missing, the FFT filter uses this frequency information to figure out what these missing sections should sound like. It's akin to looking closely at the pattern and texture of a puzzle piece and then sculpting a perfect match to fill in a missing spot. The FFT filter sees what kinds of waves (frequencies) are present before and after the gap, and then generates new waves that fit seamlessly into the existing pattern. This way, the FFT filter can reconstruct the missing audio in a way that harmonizes well with what’s already there, making the overall sound appear continuous and smooth without noticeable gaps. It's like using precision tools to craft a part that exactly fits into the audio landscape, helping to create a complete and satisfying listening experience. ASYMMETRICAL FIR FILTER Imagine an asymmetrical FIR filter used for audio interpolation like a talented but unconventional chef who specializes in creating dishes that blend unexpected flavors to fill missing ingredients. FIR stands for Finite Impulse Response, and in this context, "asymmetrical" refers to the way this filter handles data differently on either side of a given point in an audio track. Usually, filters are symmetrical, treating data before and after a point in the same way, much like a chef who balances flavors equally from both sides of a dish. However, an asymmetrical FIR filter does things a bit differently—it might weigh the audio data before a gap differently from the data after the gap. This could be useful in situations where the nature of the sound changes across the gap, like a sudden shift from a quiet verse to a loud chorus in a song. Using an asymmetrical FIR filter for audio interpolation is like having a chef who adjusts their recipe on the fly, adding more of one ingredient or less of another based on what's already in the pot to ensure the final dish is perfect. This filter meticulously crafts the sound, filling in gaps with audio that blends seamlessly with what comes before and after, even if the two sides are quite different. This approach helps maintain the natural flow and dynamics of the original audio, ensuring that the track plays smoothly without any noticeable disruptions. HALF-BAND FILTER A half-band filter for audio interpolation can be thought of as a highly specialized gardener who focuses on trimming just the top half of a hedge to make sure it blends perfectly with the bottom half. In audio terms, a "half-band" filter is designed to focus on processing only half of the frequency range available in a signal. Here's how it works: In digital audio, sounds are made up of different frequencies—some high, some low. A half-band filter cleverly splits these into two groups. It specifically targets frequencies that are up to half of the maximum frequency (that’s where the name "half-band" comes from), smoothing and blending them to fill in gaps or reconstruct parts of the audio. The filter is designed so efficiently that it ignores the other frequencies that don't need attention, focusing only on those that are critical for maintaining the audio's quality. In the context of audio interpolation—where you might have missing or incomplete audio data—the half-band filter steps in like that gardener, carefully pruning and adjusting the necessary part of the audio spectrum. It ensures that the newly interpolated audio matches smoothly with the existing sounds, particularly focusing on the essential frequencies to maintain a natural and consistent listening experience. This specialized approach makes it very effective and computationally efficient, especially suitable for tasks where high-quality audio processing is required. That is all I have created regarding most, but not all of the types of audio filters you will encounter in HQPlayer. I hope the descriptions, even if very elementary, are helpful to those beginning to understand the technical side of the audio hobby.
Any corrections, suggestions, or CONSTRUCTIVE critiques I am happy to receive at the sites listed above. Euphonic Review wishes you a great day, and a HAPPY MOTHER'S DAY (USA) tomorrow!
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