Spl Transient Designer 133
LINK ->->->-> https://geags.com/2tfbKR
Shape the summed image using a combination of an SPL transient designer and the Apple audio units graphic eq- the one that you get with garageband-Render out a mixed recording of the whole piece through a Manley pultec and then delete all the original material from existence
If you own like us one of the first versions of this beautiful device you might also have noticed that when you plug some balanced source (that share energy between hot and cold signals) in it you get a -6db level drop, indeed these versions of the SPL transient designer have unbalanced ins and outs. But they were kind enough to put balanced jack connectors which will make it easy for us to modify the inputs and making them balanced which will solve all of our level drop problems!
The KK 133 is a diffuse-field equalized pressure transducer with a free-field treble boost (4-5 dB at 12 kHz). Based on the technical design concept of the legendary Neumann M 50 microphone and its successor, the M 150, its ultra thin diaphragm is made of titanium and is spaced only a few microns from the backplate, also made of titanium. The KK 133 capsule attains an incredibly fast transient response with unprecedented accuracy.
Innovative multi-band limiting allows the cabinet to deliver maximum output and remarkably event frequency response while protecting the PS12P's speaker components. Unlike competitive powered cabinets, the Parasource Series multi-band limiting scheme ensures essential elements like vocals and solo instruments in the essential midrange band aren't being modulated by transient attacks of low frequency material like kick drums and bass tracks. This delivers an extremely smooth and precise mix even at the highest output levels the cabinet can produce.
Low-cost, higher-performance digital signal processors are now appearing on the market to satisfy the high dynamic range requirements for processing or synthesizing audio signals. How many bits are required for processing audio signals Is it 16, 20, 24, or 32 bits Does the audio application require fixed-point of floating-point arithmetic What undesirable side effects of quantization should the audio designer look out for
Depending on the complexity of the application, the audio system designer must decide on how much computational accuracy and dynamic range will be needed. The most common native data types are explained briefly in this section. 16- and 24-bit fixed-point DSPs are designed to compute integer or fractional arithmetic. 32-bit DSPs like the Analog Devices ADSP-2106x SHARC family were traditionally offered as floating point devices, however, this popular family of DSPs can equally perform both floating-point arithmetic and integer or fractional fixed-point arithmetic.
Floating-point arithmetic was traditionally used for applications that have very high dynamic range requirements, like image processing, graphics and military/space applications. The dynamic range offered for 32-bit IEEE floating-point arithmetic is 1530 dB. Typically in the past, trade-offs were considered with price vs. performance when deciding on the use of floating-point processors. Until recently, the higher cost made 32-bit floating point DSPs unreasonable for use in audio. Today, designers can achieve high-quality audio using either 32-bit fixed or floating point processing with the introduction of the lower-cost 32-bit processors like the ADSP-21161, at a cost comparable to 16-bit and 24-bit DSPs.
To achieve CD-type signal quality, the trend in recent years has been to design a system that processes audio signals digitally, using 16-bit A/D and D/A converters signal-to-noise ratio (SNR) and dynamic range around 90-93 dB. When processing these signals, the programmer should normally design the algorithm with enough computation precision that is usually greater than 16-bits in compact disk signals. CD-quality audio is just one example. For whatever the application, the audio system designer must first determine what is an acceptable SNR and then decide how much precision is required to produce acceptable results for the intended application.
In analog and digital terms, SNR (S/N ratio) and dynamic range often used synonymously. In pure analog terms, SNR is defined as the ratio of the largest known signal that exists to the noise present when no signal exists. In digital terms, SNR and dynamic range are used synonymously to describe the ratio between the largest representable number to the quantization error . A well-designed digital filter should contain a maximum signal to noise ratio (SNR) that is greater than the converter SNR. Thus, the DSP designer must be sure that the noise floor of a filter is not larger than the minimum precision required of the ADC or DAC.
As we saw in the last section, when using a DSP to process audio signals, the DSP designer must ensure that any quantization errors introduced by the arithmetic calculations executed on the processor are lower than the converter noise floor. Consider a 'CD-quality' audio system. If the DSP is to process audio data from a 16 bit A/D converter (ideal case), a 96 dB SNR must be maintained through the algorithmic process in order to maintain a CD-quality audio signal (6x16=96dB). Therefore, it is important that all intermediate calculations be performed with higher precision than the 16-bit ADC or DAC resolution . Errors introduced by the arithmetic calculations can be minimized when using larger data-word width sizes for processing audio signals. For fractional fixed-point math, we can visualize the addition of extra 'footroom' bits added to the right of the least significant bit of the input sample. The larger word sizes used in the arithmetic operations will ensure that truncation or round-off errors will be lower than the noise floor of the D/A converter, as long as 'optimal' algorithms (better filter structures) are utilized in conjunction with the larger word width.
Recent technological developments and improved knowledge of human hearing have created a demand for greater word lengths and faster sampling rates in the professional and consumer audio sectors. It has long been assumed that the human ear was capable of hearing sounds up to a frequency of about 20 kHz and was completely insensitive to frequencies above this value. This assumption was a major factor in the selection of a 44.1 kHz sampling rate. New research has suggested that many people can distinguish the quality of audio at frequencies of up to 25 kHz, and that humans are also sensitive to a degree to frequencies above even this value. This research is mainly empirical, but would mean that a substantially higher sampling frequency is necessary. D. E. Blackmer  has suggested that in order to fully meet the requirements of human auditory perception, a sound systems must be designed to cover the frequency range to up to 40 kHz (and possibly up to 80 kHz) with over 120 dB dynamic range to handle transient peaks. This is beyond the requirements of many of today's digital audio systems. As a result, 18, 20 and even 24 bit analog-to-digital converters are now widely available which are capable of exceeding the 96dB dynamic range available using 16 bits.
With many converter manufacturers introducing 24-bit A/D and D/A converters to meet emerging consumer and professional audio standards, the audio systems using these higher resolution converters will require at least 32-bit processing in order to offer sufficient precision to ensure that a filter algorithm's quantization noise artifacts will not exceed the 24-bit input signal. If optimal filter routines are used for complex processing, any quantization noise introduced in the 32-bit computations will never be seen by the 24-bit output D-A converter. In many cases, the audio designer can choose from a number of second-order structures because the result will still be greater than 120 dB. 32-bit processing will guarantee that the noise artifacts remain below the 120-dB noise floor, and hence provide a dynamic range of the audio signal up the human ear's threshold of pain. Therefore, the goal of developing robust audio algorithms is accomplished, and the only limiting factor when examining the signal quality (SNR) of the digital audio system is the precision of the 24-bit A/D and D/A converters.
While complexity for new DSP algorithms increase as audio standards and requirements are increasing, designers are looking to 18-bit, 20-bit, and 24-bit converters to increase the signal quality. A 16-bit DSP will not be adequate due to these higher resolution converter's dynamic range capabilities exceeding a 16-bit DSP processor. However, a 16-bit DSP may still be able to interface to these higher precision converters, but this would then require the use of double-precision arithmetic. Double-precision operations slow down the true performance of the processor while increasing programming complexity. Memory requirements for double-precision math are doubled. Even if double-precision math can be used, the interfaces to these higher precision converters in many cases would require glue logic to move the data to/from the DSP.
On the inserts of the kick channel, I used an API 560 to carve out some of the midrange and reduce some rumble from the low end. That was followed by an SPL transient designer to help shape the attack and sustain of the drum.
With the snare, I followed the Neve preamp with an API 550a to add some air to the top, crack to the midrange and some weight to the low end. I also used a transient designer to increase the attack and slightly extend the sustain.
The first 12 tracks on the 1608 are equipped with 550a EQs, so Willis Sound Head Engineer Jim Roll started by adding a little bit of color to the drums. On the snare, he used the hardware insert on the desk to add a Distressor which helped smooth out the transients and get some second order harmonics. He was also able to utilize the onboard bus to make a parallel drum bus compressor through a pair of analog API 525s. 153554b96e